applied sciences

Review A Review of Low-Frequency Active Control of Seat Suspension Systems

Yuli Zhao and Xu Wang * School of Engineering, RMIT University, Bundoora, VIC 3083, Australia * Correspondence: [email protected]

 Received: 17 July 2019; Accepted: 8 August 2019; Published: 13 August 2019 

Abstract: As a major device for reducing vibration and protecting passengers, the low-frequency vibration control performance of commercial vehicle seating systems has become an attractive research topic in recent years. This article reviews the recent developments in active seat suspensions for vehicles. The features of active seat suspension actuators and the related control algorithms are described and discussed in detail. In addition, the vibration control and reduction performance of active seat suspension systems are also reviewed. The article also discusses the prospects of the application of learning, including artificial neural network (ANN) control algorithms, in the development of active seat suspension systems for vibration control.

Keywords: vibration; active control; low frequency; suspension; seating system; machine learning

1. Introduction Modern research has shown the potential hazards of whole-body vibration (WBV). Based on previous studies, Krajnak (2018) [1] summarized the various diseases that may be caused by whole-body vibration, including back and neck pain, neuropathy, cardiovascular disease, digestion disorders, and even cancer. As a risky occupation, the drivers of heavy commercial vehicles are prone to prolonged exposure to low-frequency whole-body generated from road excitation, which could influence drivers’ comfort or even adversely affect their health. According to [2], the long-term operation of heavy commercial vehicles with low-frequency vibration can cause diseases of the muscles, bones, digestive system, and even the visual system. This is because low-frequency vibrations might cause resonance of the organs and tissues in the human body, and this kind of vibration energy must also be absorbed and dissipated by the body. According to [3], due to the high social cost of musculoskeletal diseases caused by working environments with low-frequency vibrations, Europe has issued regulations requiring that the vibration level of a working vehicle must be evaluated to provide a healthy and safe operation environment, where the max of 0.5 and 1.5 m/s2 are, respectively, set for 8 hours action and limit values. In [4], it was proved that the WBV affects the posture control of the human body and may cause health risks to the muscular system and spine. In [5], it was shown that if the exposure to low-frequency vibrations produced by commercial vehicles were more than 8 hours every day, conventional vehicle seating with passive suspension could not protect the driver’s body from the effects of WBVs. According to a medical research report by Johanning (2011) [6], low-frequency vibrations may cause resonances of human organs and tissues. There, it was claimed that back pain disease is one of the most common occupational injuries because the lower back of the human body is sensitive to low-frequency vibrations of 4–10 Hz. Therefore, long-term exposure to large amplitude low-frequency vibrations can cause back pain disorders, especially common diseases of the lumbar joints. These diseases include degenerative spinal changes, lumbar disc herniation, and sciatic nerve injuries, and according to ongoing medical reports, these diseases are common among tractor drivers, truck drivers, bus drivers, and other commercial heavy machinery operators who are often exposed to vibrations throughout the body.

Appl. Sci. 2019, 9, 3326; doi:10.3390/app9163326 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3326 2 of 28

The diagram (Figure1) shows the vibration amplitude of commonly used heavy commercial machinery. According to the international standard ISO 2631-1 1997, as the vertical increases, the ride comfort decreases and the WBV level increases, which also increases health risks. Therefore, the development of an efficient vibration reduction seating system is a practical and effective way to protect drivers. Researchers have studied the vibration control of vehicle seating systems as well as the various effects that vibrations and vibration transmission have on the human body. In [7], the seats of 100 commercial heavy-duty vehicles from 14 different categories were tested and evaluated for vibration isolation performance. Two groups of Seat Effective Amplitude Transmissibility (SEAT) values calculated by the weighting parameters of different standards (BS6841 and ISO2631) showed that the average SEAT values of these seats were all less than 100%, indicating that they provided a certain degree of protection. This article also proposed improving the dynamic performance of seats, which can reduce the severity of WBV exposure in many working environments. In addition to the dynamic characteristics of seats, the dynamic response of the human body under vibration excitation is also a topic of considerable interest. In [8], 41 male and 39 female subjects between 18 and 65 years of age were selected to participate in an experiment to study the factors that may affect the apparent of the human body, which is a method that can be used to present comfort levels and to estimate WBV levels. According to this study, aging can affect the resonant frequency of the human body and the transmission ratio of vibration. Further, gender and body mass index (BMI) are also factors that affect vibration transmissibility. In addition to research on the whole seat, the seat cushion, as an important vibration isolation device, was examined in [9]. There, they investigated the seat cushion–body interaction by measuring and analyzing the contact force distribution and the contact area between the human body and the seat cushion when vibration is experienced. It was found that the pressure distribution at the interface between the body and the cushion showed strong asymmetry in terms of dynamic contact force, and the effective contact area was affected by the nonlinear characteristics of the cushion itself and the characteristics of the soft tissue of the human body. Further, under large vibration excitation, a seat cushion with high stiffness, a large coefficient, and large static deflection is able to effectively reduce the transmission of vibration. Appl. Sci. 2019, 9, x FOR PEER REVIEW 3 of 30

Figure 1. Vibration measurement examples of on- and off-road vehicles [6]. Figure 1. Vibration measurement examples of on- and off-road vehicles [6].

In terms of seat vibration isolation measurements, Mozaffarin et al. (2008) [10] designed an active dummy technique (Figure 2), which adopted lateral and longitudinal actuators to produce forces in the vertical and lateral directions, respectively, to simulate the dynamic response of three different human body by reproducing the equivalent dynamic mass.

Figure 2. The schematic diagram of MEMOSIK V [10].

Appl. Sci. 2019, 9, x FOR PEER REVIEW 3 of 30

Appl. Sci. 2019, 9, 3326 3 of 28 Figure 1. Vibration measurement examples of on- and off-road vehicles [6].

InIn termsterms ofof seatseat vibrationvibration isolationisolation measurements,measurements, Moza Mozaffarinffarin et et al. al. (2008)(2008) [[10]10] designeddesigned anan activeactive dummydummy techniquetechnique (Figure(Figure2 2),), which which adopted adopted lateral lateral and and longitudinal longitudinal actuators actuators to to produce produce forces forces in in thethe verticalvertical andand laterallateral directions,directions, respectively,respectively, toto simulatesimulate thethe dynamicdynamic responseresponse ofof threethree didifferentfferent humanhuman bodybody massesmasses byby reproducingreproducing thethe equivalentequivalent dynamicdynamic mass.mass.

FigureFigure 2.2. TheThe schematicschematic diagramdiagram ofof MEMOSIKMEMOSIK VV [[10].10].

Of course, in all of these works, determining how to control vibration is the most critical aspect. Three current mainstream research directions in this area are passive, semiactive, and active vibration controls. Passive vibration control of a seating system can reduce vibration by using conventional spring and damper components in the seat suspension system, but due to its characteristic limitations, vibration control that targets multiple frequencies cannot be achieved even with a well-tuned traditional positive spring–damper system. Therefore, a quasi-zero static stiffness seat suspension system based on the combination of negative stiffness and positive stiffness springs was proposed by Le and Ahn (2013) [11] to improve the vibration control efficiency of a seating system, since the characteristics of high static stiffness and low dynamic stiffness can be used to eliminate seat vibration. In [12], a poly-optimal solution was sought for a seating system combined with a pneumatic spring and damper. This improved the performance of the traditional passive seating system by attenuating low-frequency vibrations in the frequency range of 0–4 Hz. Semiactive vibration control of a seating system utilizes the characteristics of magnetorheological [13–16] and the electrorheological materials [17], which can change the stiffness or Young’s modulus under magnetic field variations and achieve vibration control in a specific frequency range. In [18], a negative stiffness seat suspension system combined with a pneumatic spring and stiffness control mechanism was proposed, and the related control algorithm that affects device stiffness variations associated with position and velocity data evaluation was designed. The semiactive vibration control method can achieve vibration control in a relatively certain frequency bandwidth with less energy consumption and fewer costs than the other two methods. In [19], the difference between seating systems with active electromagnetic seat suspension and passive seat air suspension in reducing the WBV level and improving comfort was compared. The experimental results showed that the active electromagnetic suspension performed better at vibration reduction than the passive air spring suspension. In particular, passive suspensions may increase the amplitude of lateral vibration, which also has a negative effect on driver comfort. In terms of structure, in addition to traditional shock absorbers, semiactive seat suspensions have also been designed in different styles. Bai et al. (2017) [14] designed an integrated semiactive seat Appl. Sci. 2019, 9, 3326 4 of 28

suspension that included a swing mechanism (Figure3) that converts longitudinal and vertical motion into rotational motion and a torque-controlled rotary magnetorheological (MR) damper operated in a pure shear mode to attenuate vertical and longitudinal vibrations. Ning et al. (2019) [20] proposed a new semiactive seat suspension based on the variable admittance (VA) concept and designed a rotating VA device based on the MR damper. A random vibration test showed that the semiactive seat suspension had excellent low-frequency vibration cancellation performance. The frequency-weighted root-mean-square (FW-RMS) acceleration of the seat was reduced by 43.6%, indicating that ride comfort was greatly improved. Ning et al. (2018) [21] developed a semiactive vibration control seating system based on an energy harvest device with variable external resistance. The energy regeneration seat device included a three-phase generator and a gear reducer mounted at the centre of the scissor-like structure of the seat, and the vibrational energy was collected directly from the rotational motion of the scissor-like structure. An H-infinity-state controller was designed for a semiactive vibration control seating system, and the FW-RMS acceleration was reduced by 22.84% compared with passive Appl. Sci. 2019, 9, x FOR PEER REVIEW 5 of 30 vehicle suspension. At the same time, the generated RMS power was 1.21 W.

FigureFigure 3. 3. SemiactiveSemiactive seating seating system system vibration vibration controls controls with with a a magnetorheological magnetorheological (MR) (MR) damper damper [14]. [14].

ForFor the the controller controller design, design, Sky-hook Sky-hook control control theory theory [22–25], [22–25], H-infinity H-infinity control control algorithm algorithm [26–32], [26–32], nonresonancenonresonance theory theory [15] [15], , on-oon-offff controlcontrol strategystrategy [[33],33], fuzzyfuzzy controlcontrol theorytheory[34–36], [34–36], optimal optimal control control theorytheory [37–39], [37–39], lyapunov lyapunov control control scheme [40], [40], PIDPID control algorithmalgorithm[41] [41] and and sliding-mode sliding-mode control control algorithmalgorithm [42] [42] are are applied applied for for the the design design of of the the controllers. controllers. ComparedCompared with with controller controller designs designs based based on on a single a single traditional traditional adaptive adaptive control control algorithm, algorithm, an increasingan increasing number number of controllers of controllers integrating integrating multiple multiple control algorithms control algorithms with semiactive with semiactive vibration controlvibration seating control systems seating have systems been have proposed been proposed to improve to improvevibration vibration attenuation attenuation performance. performance. Phu et al.Phu (2015) et al. [43] (2015) developed [43] developed a new adaptive a new fuzzy adaptive controller fuzzy combining controller the combining H-infinity the and H-infinity sliding-mode and controlsliding-mode algorithms control for a algorithms semiactive forseat a suspension semiactive with seat suspensionan MR fluid withdamper. an MRThis fluid controller damper. features This acontroller fuzzy control features method a fuzzy that control does not method require that an does accurate not require dynamic an accuratemodel, even dynamic in a dynamic model, even system in a withdynamic an uncertain system with environment. an uncertain Phu environment. et al. (2017)Phu [44] et al.designed (2017) [44a new] designed adaptive a new hybrid adaptive controller hybrid integratingcontroller integrating the H-infinity the H-infinity control controlalgorithm, algorithm, the sliding-mode the sliding-mode control control algorithm, algorithm, and and the the Proportional-Intergral-DerivativeProportional-Intergral-Derivative (PID)(PID) control control algorithm algorithm with with the vibration the vibration attenuation attenuation of a semiactive of a semiactiveseating system. seating This system. controller This features controller a combination features a combination of the Hurwitz ofconstant the Hurwitz matrix constant as components matrix as of componentsthe sliding surface of the andsliding the H-infinitysurface and algorithm the H-infinity with robust algorithm stability. with In robust addition, stability. a fuzzy In logic addition, module a fuzzybased logic on the module interval based type-2 on the fuzzy interval logic type-2 system fu waszzy established,logic system andwas aestablished, model was and characterized a model was by characterizedon-line clustering by on-line considering clustering external considering interference. external Phu interference. et al. (2017) Phu [45 et] proposedal. (2017) [45] a new proposed hybrid acontroller new hybrid combining controller a neuralcombining fuzzy a controlneural fuzzy module, control a Proportional-Integral module, a Proportional-Integral (PI) control module, (PI) control and a module,sliding mode and a control sliding module mode tocontrol control module a semiactive to control seat suspension a semiactive with seat an suspension MR damper. with The intervalan MR damper. The interval type-2 fuzzy model with an on-line rule updating function was adopted, and a granular clustering method was used to find data for the initial fuzzy set used to support the fuzzy model. Compared with conventional controllers, the proposed controller can provide better stability for vibration control performance. Nguyen et al. (2015) [46] designed a novel neuro-fuzzy controller (NFC) for a semiactive seat suspension system with an MR damper. This adaptive neuro-fuzzy inference system (ANFIS) is based on an algorithm called B-ANFIS, which is combined with a fuzzy inference system (FIS). Compared with the skyhook control theory, the NFC is better at improving the ride comfort of the vehicle. In addition, the NFC’s ability to track trajectories and transient response characteristics is superior to that of conventional skyhook controllers. Phu et al. (2017) [47] proposed a new adaptive fuzzy controller based on inversely fuzzified values related to the H-infinity control algorithm to control the vibration of a semiactive seat suspension system with an MR damper and a Riccati-like equation with fuzzified values to enhance system robustness. This paper aims to review and classify the active vibration control of seating systems proposed in previous works that have active seat suspension. In addition, the algorithms and structures of the active controllers are analyzed and discussed. Finally, the development of artificial neural network

Appl. Sci. 2019, 9, 3326 5 of 28 type-2 fuzzy model with an on-line rule updating function was adopted, and a granular clustering method was used to find data for the initial fuzzy set used to support the fuzzy model. Compared with conventional controllers, the proposed controller can provide better stability for vibration control performance. Nguyen et al. (2015) [46] designed a novel neuro-fuzzy controller (NFC) for a semiactive seat suspension system with an MR damper. This adaptive neuro-fuzzy inference system (ANFIS) is based on an algorithm called B-ANFIS, which is combined with a fuzzy inference system (FIS). Compared with the skyhook control theory, the NFC is better at improving the ride comfort of the vehicle. In addition, the NFC’s ability to track trajectories and transient response characteristics is superior to that of conventional skyhook controllers. Phu et al. (2017) [47] proposed a new adaptive fuzzy controller based on inversely fuzzified values related to the H-infinity control algorithm to control the vibration of a semiactive seat suspension system with an MR damper and a Riccati-like equation with fuzzified values to enhance system robustness. This paper aims to review and classify the active vibration control of seating systems proposed in previous works that have active seat suspension. In addition, the algorithms and structures of the active controllers are analyzed and discussed. Finally, the development of artificial neural network (ANN) controllers and their applications in active vibration control in seating systems are also discussed.

2. Seating Systems with Active Suspension

2.1. Experiments with Prototypes Active seat suspension prototypes in several research projects have used electromagnetic, hydraulic, or air actuators to generate a corresponding compensation force for vibration cancellation in a finite number of frequencies, thereby reducing the vibration acceleration amplitude and improving the comfort of the seating system. According to [48], using an active vibration control suspension in a vehicle has better vibration cancellation performance than passive seat suspension. In [49], comparative experiments demonstrated that active and semiactive seat suspensions could improve comfort by approximately 50% compared with passive seat suspensions. The actuators used for active seat suspensions generally fall into three categories: electromagnetic actuators using linear or conventional rotating motors, hydraulic actuators using hydraulic servos, and air actuators using air springs. Among these three types of actuators, electromagnetic actuators have attracted the most attention because they have good dynamic responses; do not require an additional hydraulic servo system, tubes, or compressors; and have small space requirements. The large output of hydraulic actuators makes them easy to carry with greater mass. The air actuator has a good vibration isolation effect in high frequencies.

2.1.1. Pneumatic Actuator Stein (1997) [50] developed an active seat suspension using a pneumatic spring and a corresponding feedback controller. According to the results, the active seat suspension using vibration compensation can reduce the vibration amplitude by 10 dB, which is about 3 times. The vibration transmission rate is reduced by 30%–40% compared with a conventional passive seat suspension. In another research project, Stein (1997) [51] proposed an active vibration control seating system to attenuate low-frequency vibration with an active seat suspension (Figure4) composed of a pneumatic spring and a related linear control system. The conventional metal spring in the system is used to carry the static load, and the pneumatic spring is used to generate damping and compensating forces to mitigate vibration. The pneumatic spring is driven by compressed air which is controlled through a proportional valve. The benefit of such a parallel structure is that the energy consumption of the whole system can be reduced. Two acceleration sensors and one displacement sensor were used in the experimental equipment, and the electric signals generated by the excitation were respectively collected by the corresponding controller and summed at the sum point to control the actuator. There is no actual damper in this system, and the function of the damper is replaced by absolute damping, Appl. Sci. 2019, 9, 3326 6 of 28 which is generated from the air spring through the skyhook control method. This active suspension needs to consider the complex state and parameters of the entire pneumatic subsystem and take into account the state equation of compressed air. In addition, the flow rate of the air, the corresponding pressure changes in the air spring and the corresponding forces are also considered. For the control system, a simple and practical linear controller system is used that is based on feed-forward and feed-back algorithms to reduce the vibration effect on the human body by controlling the static position, damping force, and compensation force of the seating system. Researchers have found that this active pneumatic spring seat suspension can reduce vibration transmissibility by approximately 8–10 dB with the feed-forward compensation path for a mass of 80 kg. In an experiment, by adjusting the parameters of the pneumatic spring, it was found that the active vibration control of the seating system

Appl.performed Sci. 2019 well, 9, x FOR in a PEER frequency REVIEW range less than 4 Hz. However, this control system needs to work7 under of 30 an ideal condition.

Figure 4. Active seating system vibration controls with a parallel spring structure [51]. Figure 4. Active seating system vibration controls with a parallel spring structure [51]. An active seat suspension incorporating a hydraulic shock absorber and an active pneumatic springAn (Figure active 5seat) was suspension developed incorporating in [ 52–54]. Ina hydr thisaulic seating shock system, absorber a hydraulic and an shockactive absorberpneumatic is springconnected (Figure to a 5) scissor-like was developed frame in for [52–54]. vibrational In th energyis seating absorption, system, a whilehydraulic a pneumatic shock absorber spring is connectedattached to to the a bottomscissor-like of the frame seat and for avibrational rod of the frameenergy to absorption, produce the while corresponding a pneumatic compensation spring is attachedforce. The to airthe springbottom is of inflated the seat and and deflated a rod of bythe usingframe compressedto produce the air corresponding and a proportional compensation air valve. force.The advantage The air spring of this is configuration inflated and is deflated that configurations by using compressed of commonly air existing and a proportional vehicle seating air systems valve. Thecan beadvantage used without of this large configuration modifications. is that Based configurations on this type activeof commonly seat suspension existing system,vehicle aseating robust systemscontroller can (Figure be used6) without that can large work mo withdifications. di fferent Based mass on loads this type was active proposed seat bysuspension Maciejewski system, etal. a robust(2010) [controller52] for an active(Figure seat 6) suspension.that can work In with the design different of the mass controller, loads was the authorproposed used by a Maciejewski triple feedback et al.loop (2010) system [52] to for detect an theactive acceleration, seat suspension. the relative In th speed,e design and of the the displacement controller, the of the author suspension used a system triple feedbackand to control loop thesystem system. to detect The results the acceleration, of the experiment the relative demonstrated speed, and that the active displacement vibration controlof the suspensionof this seating system system and could to control be used theto system. reduce The the results amplitude of the of experiment the vibration demonstrated by half compared that active with vibrationconventional control passive of this seating seating systems system at could a resonant be used frequency. to reduce The the systemamplitude can of control the vibration the suspension by half comparedwell in the with range conventional of 0.5–4 Hz and passive can reduceseating response systems amplitudesat a resonant under frequency. different The mass system load can conditions. control the suspension well in the range of 0.5–4 Hz and can reduce response amplitudes under different mass load conditions.

Figure 5. A seating system with an active pneumatic spring suspension [52].

Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 30

Figure 4. Active seating system vibration controls with a parallel spring structure [51].

An active seat suspension incorporating a hydraulic shock absorber and an active pneumatic spring (Figure 5) was developed in [52–54]. In this seating system, a hydraulic shock absorber is connected to a scissor-like frame for vibrational energy absorption, while a pneumatic spring is attached to the bottom of the seat and a rod of the frame to produce the corresponding compensation force. The air spring is inflated and deflated by using compressed air and a proportional air valve. The advantage of this configuration is that configurations of commonly existing vehicle seating systems can be used without large modifications. Based on this type active seat suspension system, a robust controller (Figure 6) that can work with different mass loads was proposed by Maciejewski et al. (2010) [52] for an active seat suspension. In the design of the controller, the author used a triple feedback loop system to detect the acceleration, the relative speed, and the displacement of the suspension system and to control the system. The results of the experiment demonstrated that active vibration control of this seating system could be used to reduce the amplitude of the vibration by half compared with conventional passive seating systems at a resonant frequency. The system can control the suspension well in the range of 0.5–4 Hz and can reduce response amplitudes under different Appl. Sci. 2019, 9, 3326 7 of 28 mass load conditions.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 8 of 30 Figure 5.5. A seating system with an active pneumatic spring suspension [[52].52].

Figure 6. Schematic of the triple feedback loop controller [52]. Figure 6. Schematic of the triple feedback loop controller [52]. With the same active seat suspension, Maciejewski et al. (2014) [54] proposed an adaptive controller With the same active seat suspension, Maciejewski et al. (2014) [54] proposed an adaptive for an active seat suspension (Figure7). This is a multicontroller approach to control the entire system, controller for an active seat suspension (Figure 7). This is a multicontroller approach to control the where the primary controller is used to calculate the force required to reduce the vibration. The inverse entire system, where the primary controller is used to calculate the force required to reduce the model is used to calculate the effective area of the proportional control valve. The application of the vibration. The inverse model is used to calculate the effective area of the proportional control valve. inverse model in the controller can directly derive the input signal according to the required force. The application of the inverse model in the controller can directly derive the input signal according The Proportional-Derivative (PD) predictor is used to generate the corresponding control signal to to the required force. The Proportional-Derivative (PD) predictor is used to generate the speed up the controller. Finally, the adaptive mechanism is able to estimate the load mass based on the corresponding control signal to speed up the controller. Finally, the adaptive mechanism is able to inflation and deflation of the pneumatic spring. This active seat suspension is able to achieve good estimate the load mass based on the inflation and deflation of the pneumatic spring. This active seat vibration control performance compared with a passive system, having a load range from 50 to 150 kg suspension is able to achieve good vibration control performance compared with a passive system, at a resonant frequency of 1.3 Hz. The advantage of this controller is that the adaptive control itself having a load range from 50 to 150 kg at a resonant frequency of 1.3 Hz. The advantage of this makes the system quickly return to stability by estimating the initial suspended load. The shortcoming controller is that the adaptive control itself makes the system quickly return to stability by estimating of the controller is that the complexity of the multicontroller may delay signals, and in the case of high the initial suspended load. The shortcoming of the controller is that the complexity of the road roughness, the vibration control performance of the seating system is degraded. multicontroller may delay signals, and in the case of high road roughness, the vibration control performance of the seating system is degraded.

Figure 7. Schematic of the multicontroller [54].

Maciejewski et al. (2018) [55] developed a horizontal active seat suspension using pneumatic muscles (Figure 8) for horizontal vibration control. This original control system (Figure 9) combined a primary controller and an inverse model module to provide a control signal to the pneumatic muscles, and a PD control module was used to speed up the signal. According to the final results, the proposed active seat suspension performed better than a passive seat suspension for vibration attenuation in the 1–10 Hz frequency range.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 8 of 30

Figure 6. Schematic of the triple feedback loop controller [52].

With the same active seat suspension, Maciejewski et al. (2014) [54] proposed an adaptive controller for an active seat suspension (Figure 7). This is a multicontroller approach to control the entire system, where the primary controller is used to calculate the force required to reduce the vibration. The inverse model is used to calculate the effective area of the proportional control valve. The application of the inverse model in the controller can directly derive the input signal according to the required force. The Proportional-Derivative (PD) predictor is used to generate the corresponding control signal to speed up the controller. Finally, the adaptive mechanism is able to estimate the load mass based on the inflation and deflation of the pneumatic spring. This active seat suspension is able to achieve good vibration control performance compared with a passive system, having a load range from 50 to 150 kg at a resonant frequency of 1.3 Hz. The advantage of this controller is that the adaptive control itself makes the system quickly return to stability by estimating the initial suspended load. The shortcoming of the controller is that the complexity of the

Appl.multicontroller Sci. 2019, 9, 3326 may delay signals, and in the case of high road roughness, the vibration control8 of 28 performance of the seating system is degraded.

FigureFigure 7. 7.Schematic Schematic of of the the multicontroller multicontroller [ 54[54].].

MaciejewskiMaciejewski etet al.al. (2018)(2018) [[55]55] developeddeveloped aa horizontalhorizontal activeactive seatseat suspension suspension using using pneumatic pneumatic musclesmuscles (Figure (Figure8) 8) for for horizontal horizontal vibration vibration control. control. This This original original control control system system (Figure (Figure9) combined 9) combined a primarya primary controller controller and and an inverse an inverse model model module module to provide to provide a control a signalcontrol to signal the pneumatic to the pneumatic muscles, andmuscles, a PD controland a PD module control was module used towas speed used up to the speed signal. up the According signal. According to the final to results, the final the results, proposed the activeproposed seat suspensionactive seat performedsuspension better performed than a passive better seatthan suspension a passive forseat vibration suspension attenuation for vibration in the Appl. Sci. 2019, 9, x FOR PEER REVIEW 9 of 30 Appl.1–10attenuation Sci. Hz 2019 frequency, 9 ,in x FORthe range.PEER1–10 REVIEWHz frequency range. 9 of 30

FigureFigure 8.8. (((aaa))) TheThe The pneumaticpneumatic pneumatic musclemuscle muscle atat at thethe the nominalnominal lengthlength andand (( bb)) AfterAfter contractioncontraction [55].[55]. [55].

FigureFigure 9.9. BlockBlockBlock diagramdiagram diagram ofof of thethe the controlcontrol control structurestructure ofof thethe vibrationvibration controlcontrol system system [55]. [[55].55].

TheThe utilizationutilization ofof pneumaticpneumatic springssprings asas actuatorsactuators hashas somesome advantages,advantages, suchsuch asas beingbeing simple,simple, reliable,reliable, andand compact.compact. AtAt thethe samesame time,time, theirtheir loloww responseresponse speed,speed, poorpoor controlcontrol precision,precision, andand dependencedependence onon aa compressorcompressor pipelinepipeline hinderhinder theirtheir actualactual use.use.

2.1.2.2.1.2. HydraulicHydraulic ActuatorActuator SteinStein andand BalloBallo (1991)(1991) [56][56] developeddeveloped anan activeactive seatseat suspensionsuspension (Figure(Figure 10)10) usingusing aa hydraulichydraulic actuator.actuator. InIn thisthis system,system, thethe hydraulichydraulic actuatoractuator isis controlledcontrolled byby aa solenoidsolenoid valvevalve toto controlcontrol thethe directiondirection ofof thethe forceforce generatedgenerated byby thethe actuatoractuator.. TheThe controlcontrol systemsystem generatesgenerates aa correspondingcorresponding compensationcompensation forceforce toto reducereduce thethe vibrationvibration basedbased onon signalssignals collectedcollected byby twotwo accelerationacceleration sensorssensors andand aa displacementdisplacement sensor.sensor. AA PIPI controllercontroller waswas desidesignedgned forfor thethe activeactive seatseat suspension.suspension. SinceSince thethe accelerationacceleration sensorsensor andand amplifieramplifier areare integratedintegrated forfor thethe displacementdisplacement ofof thethe positioningpositioning system,system, thethe systemsystem cancan bebe simplifiedsimplified usingusing onlyonly oneone acceleromeaccelerometerter placedplaced onon thethe chassis.chassis. ThisThis structurestructure greatlygreatly simplifiessimplifies thethe controlcontrol system.system. ThisThis structurestructure cancan alalsoso bebe achievedachieved byby connectingconnecting twotwo first-orderfirst-order low-low- passpass filtersfilters inin seriesseries oror byby usingusing aa second-ordersecond-order ananalogalog circuitcircuit toto adoptadopt aa veryvery lowlow resonantresonant frequencyfrequency (Figure(Figure 11).11).

Appl. Sci. 2019, 9, 3326 9 of 28

The utilization of pneumatic springs as actuators has some advantages, such as being simple, reliable, and compact. At the same time, their low response speed, poor control precision, and dependence on a compressor pipeline hinder their actual use.

2.1.2. Hydraulic Actuator Stein and Ballo (1991) [56] developed an active seat suspension (Figure 10) using a hydraulic actuator. In this system, the hydraulic actuator is controlled by a solenoid valve to control the direction of the force generated by the actuator. The control system generates a corresponding compensation force to reduce the vibration based on signals collected by two acceleration sensors and a displacement sensor. A PI controller was designed for the active seat suspension. Since the acceleration sensor and amplifier are integrated for the displacement of the positioning system, the system can be simplified using only one accelerometer placed on the chassis. This structure greatly simplifies the control system. Appl.This Sci. structure 2019, 9, x FOR can alsoPEERbe REVIEW achieved by connecting two first-order low-pass filters in series or by10 using of 30 a second-orderAppl. Sci. 2019, 9,analog x FOR PEER circuit REVIEW to adopt a very low resonant frequency (Figure 11). 10 of 30

Figure 10. Active hydraulic control of a seat suspension system [56]. Figure 10. Figure 10. Active hydraulic control of aa seatseat suspensionsuspension systemsystem [[56].56].

Figure 11. Schematic of the control system [56]. Figure 11. Schematic of the control system [56]. In this study, the delay ofFigure hydraulic 11. Schematic performance of the control was also system considered [56]. and solved by using the cut-oInff thisfrequency study, the method. delay of In hydraulic addition, performa the hysteresisnce was eff ectalso of considered the hydrodynamic and solved device by using was the also cut-offcarefully Infrequency this considered study, method. the in delay the In processaddition,of hydraulic of the controller performa hysteres design.isnce effect was Depending ofalso the considered hydrodynamic on the and result, solved device the by activewas using also seat the carefullysuspensioncut-off frequencyconsidered with the method. in controller the processIn canaddition, reduce of controller the the hysteres acceleration design.is effect Depending Power of Spectralthe hydrodynamicon Densitythe result, (PSD) thedevice amplitudeactive was seat also up suspensiontocarefully 16 dB at consideredwith a frequency the controller in of the 2 Hz process can compared reduce of controller the with acce a passiveleration design. system. Power Depending Spectral on Density the result, (PSD) the amplitude active seat upsuspension to 16 dB at with a frequency the controller of 2 Hz can compared reduce the with acce a passiveleration system. Power Spectral Density (PSD) amplitude up Activeto 16 dB hydraulic at a frequency control of has 2 Hz the compared advantages with of aa passivelarge output system. force and high control precision, but theActive huge hydraulic hydraulic control pipeline has andthe advantagesservo system of a oflarge hydraulic output forcecontrol and also high greatly control limitprecision, its application.but the huge hydraulic pipeline and servo system of hydraulic control also greatly limit its application. 2.1.3. Electromagnetic Actuator 2.1.3.A newElectromagnetic active seating Actuator vibration reduction structure (Figure 12) was proposed in [57–60]. This seatingA system new active is based seating on the vibration common reduction scissor-like struct structureure (Figure of commercial 12) was proposedvehicle seating in [57–60]. systems, This usingseating an inexpensivesystem is based conventional on the common rotary scissor-likeelectric motor structure instead of of co anmmercial expensive vehicle linear seating motor systems, as the actuator.using an The inexpensive 1:40 gearbox conventional allows the rotary 400 W electric Panasonic motor DC instead motor ofto anproduce expensive a torque linear output motor of as 52 the Nm.actuator. Moreover, The 1:40due gearboxto the enlarged allows thegearbox, 400 W the Panasonic internal DCfriction motor of theto produce active suspension a torque output is greater of 52 thanNm. that Moreover, of a conventional due to the suspension enlarged gearbox, system. theThe intern systemal frictioncan save of some the active space suspensionbecause there is greateris no needthan to that install of a a conventional conventional suspensionshock absorber. system. In addiThe tion,system the can spring save stiffness some space of the because seat is carefullythere is no selectedneed to to install keep thea conventional resonant frequency shock absorber. of whole In seating addition, system the spring lower stiffnessthan 4 Hz, of whichthe seat is isthe carefully most sensitiveselected vibration to keep the frequency resonant range frequency of human of whole body seatingand may system cause loweran uncomfortable than 4 Hz, which feeling. is the most sensitive vibration frequency range of human body and may cause an uncomfortable feeling.

Appl. Sci. 2019, 9, 3326 10 of 28

Active hydraulic control has the advantages of a large output force and high control precision, but the huge hydraulic pipeline and servo system of hydraulic control also greatly limit its application.

2.1.3. Electromagnetic Actuator A new active seating vibration reduction structure (Figure 12) was proposed in [57–60]. This seating system is based on the common scissor-like structure of commercial vehicle seating systems, using an inexpensive conventional rotary electric motor instead of an expensive linear motor as the actuator. The 1:40 gearbox allows the 400 W Panasonic DC motor to produce a torque output of 52 Nm. Moreover, due to the enlarged gearbox, the internal friction of the active suspension is greater than that of a conventional suspension system. The system can save some space because there is no need to install a conventional shock absorber. In addition, the spring stiffness of the seat is carefully selected to keep the resonant frequency of whole seating system lower than 4 Hz, which is the most sensitive vibration Appl.frequency Sci. 2019 range, 9, x FOR of humanPEER REVIEW body and may cause an uncomfortable feeling. 11 of 30

(a)

(b)

Figure 12. (a)(a) The schematic schematic of of the the seat seat structure structure (b) (b) Front Front view view of of the seat structure [58]. [58].

In terms of the control control system, an H-infinity H-infinity al algorithmgorithm based on output feedback feedback was developed developed by Ning et al. (2016) [57] [57] to reduce the seating systemsystem vibration. The The feature feature of this project was the estimation of the friction generated by the active vibration control system. A special controller with friction compensation was was employed employed to to improve improve th thee precision of the control, which can affect affect the performance of the vibration contro control.l. The The final final test results showed that in the low-frequency range, from 3 to 5 Hz, the driver’s body RMS accele accelerationration can be reduced by more thanthan 35%.35%. In another study, NingNing etet al.al. (2016) (2016) [58] [58] prop proposedosed an an H-infinity H-infinity controller with friction compensation to to control control the the previously previously mentioned mentioned active active seat seat suspension. suspension. Due Due to the to theusage usage of the of frictionthe friction observer observer based based on onthe the acceleration acceleration meas measurement,urement, the the H-infinity H-infinity controller controller can can be be more sensitive to the response of vibration excitation and can also improve the vibration control performance. The results of the experiment showed that the entire vibration control system could reduce vibration very well in a frequency range from 1 to 4.5 Hz. According to the ISO 2631 standard, the FW-RMS value of the vibration is reduced up to 35.5% by the vibration control system compared with a conventional well-tuned passive suspension system. The RMS energy consumption of 3.82 Watts indicates that the system’s energy usage is very low. In [59], a terminal sliding mode controller based on a space observer and a disturbance observer was proposed by Ning et al. (2017) to control an active seat suspension system (Figure 13). In the case where the suspension acceleration and relative displacement are measurable, but the absolute seat speed is immeasurable, the settings of the disturbance and state observers can reduce the switching gain of the controller. The controller proposed in this paper had better control performance than the state feedback terminal sliding mode controller and could improve the comfort of the seating system. According to the experimental results, FW-RMS and Vibration Dose Value (VDV) were reduced by 34% and 33%, respectively.

Appl. Sci. 2019, 9, 3326 11 of 28 sensitive to the response of vibration excitation and can also improve the vibration control performance. The results of the experiment showed that the entire vibration control system could reduce vibration very well in a frequency range from 1 to 4.5 Hz. According to the ISO 2631 standard, the FW-RMS value of the vibration is reduced up to 35.5% by the vibration control system compared with a conventional well-tuned passive suspension system. The RMS energy consumption of 3.82 Watts indicates that the system’s energy usage is very low. In [59], a terminal sliding mode controller based on a space observer and a disturbance observer was proposed by Ning et al. (2017) to control an active seat suspension system (Figure 13). In the case where the suspension acceleration and relative displacement are measurable, but the absolute seat speed is immeasurable, the settings of the disturbance and state observers can reduce the switching gain of the controller. The controller proposed in this paper had better control performance than the state feedback terminal sliding mode controller and could improve the comfort of the seating system. According to the experimental results, FW-RMS and Vibration Dose Value (VDV) were reduced by 34% andAppl.Appl. 33%,Sci. Sci. 2019 2019 respectively., ,9 9, ,x x FOR FOR PEER PEER REVIEW REVIEW 1212 ofof 3030

FigureFigure 13. 13. The The experimental experimental setupsetup ofof thethe systemsystem withwith the the terminal terminal sliding sliding mode mode controller controller [[59]. 59[59].].

With the same active seat suspension system, a disturbance observer based on the Takagi–Sugeno WithWith thethe samesame activeactive seatseat suspensionsuspension systemsystem, , aa disturbancedisturbance observerobserver basedbased onon thethe Takagi–Takagi– (TS) fuzzy controller was proposed by Ning et al. (2017) [60] for active seat suspension vibration control SugenoSugeno (TS)(TS) fuzzyfuzzy controllercontroller waswas proposedproposed byby NingNing etet al.al. (2017)(2017) [60][60] forfor activeactive seatseat suspensionsuspension (Figure 14). The controller used a closed-loop feedback control with acceleration and seat suspension vibrationvibration controlcontrol (Figure(Figure 14).14). TheThe controllercontroller usedused aa closed-loopclosed-loop feedbackfeedback controlcontrol withwith accelerationacceleration displacement measurement signals to achieve good adaptability and robustness. The disturbance andand seat seat suspension suspension displacement displacement measurement measurement sign signalsals to to achieve achieve good good adaptability adaptability and and robustness. robustness. observer can estimate disturbances caused by friction and model simplification. The TS fuzzy control TheThe disturbance disturbance observer observer can can estimate estimate disturbances disturbances caused caused by by friction friction and and model model simplification. simplification. The The improves the vibration reduction performance of the controller by estimating load changes. During TSTS fuzzyfuzzy controlcontrol improvesimproves thethe vibrationvibration reductionreduction performanceperformance ofof thethe controllercontroller byby estimatingestimating loadload the experiment, the controller worked well in vibration frequencies below 4 Hz. Two different loads changes.changes. During During the the experiment, experiment, the the controller controller worked worked well well in in vibration vibration frequencies frequencies below below 4 4 Hz. Hz. Two Two of 55 and 70 kg could achieve a good response through the controller. The active seat suspension differentdifferent loads loads of of 55 55 and and 70 70 kg kg could could achieve achieve a a good good response response through through the the controller. controller. The The active active seat seat control was able to reduce the RMS acceleration by more than 45% compared to a well-tuned passive suspensionsuspension controlcontrol waswas ableable toto reducereduce thethe RMSRMS acceaccelerationleration byby moremore thanthan 45%45% comparedcompared toto aa well-well- seat suspension. tunedtuned passive passive seat seat suspension. suspension.

FigureFigure 14.14. Block Block diagram diagram of of the the Takagi–Sugeno Takagi–Sugeno (TS) (TS) controllerco controllerntroller withwith with aa a disturbancedisturbance disturbance observerobserver observer [[60]. 60[60].].

BasedBased on on previous previous research, research, Ning Ning et et al. al. (2018) (2018) [61] [61] proposed proposed active active vibration vibration control control of of a a multi- multi- degree-of-freedomdegree-of-freedom (multi-DOF)(multi-DOF) seatingseating systemsystem withwith aa double-layerdouble-layer structurestructure (Figure(Figure 15)15) andand anan associatedassociated controller. controller. A A second second layer layer was was added added to to the the preceding preceding scissor-like scissor-like structure structure to to control control the the vibrationvibration ofof pitchingpitching andand rocking.rocking. TheThe secondsecond layelayerr consistsconsists ofof aa universaluniversal jointjoint andand fourfour supportsupport springs.springs. TheThe universaluniversal jointjoint connectsconnects twotwo conventionalconventional rotatingrotating motorsmotors andand aa gearboxgearbox toto generategenerate aa torquetorque ofof 52 52 Nm Nm toto removeremove thethe rollingrolling and and pitchingpitching vibrations. vibrations. TheThe springspring systemsystem isis responsibleresponsible for for supportingsupporting thethe staticstatic load.load. ThisThis isis aa simplesimple andand efficientefficient activeactive vibrationvibration controlcontrol ofof aa multi-DOFmulti-DOF seatingseating system system compared compared with with other other active active vibration vibration controls controls of of multi-DOF multi-DOF seating seating systems. systems. For For the the controlcontrol system,system, aa slidingsliding modemode controllercontroller toto reducereduce thethe rollroll vibrationvibration waswas designed.designed. ThisThis algorithmalgorithm hashas the the advantages advantages of of fast fast convergence convergence and and good good ro robustnessbustness and and can can be be used used to to control control the the vibration vibration inin the the swinging swinging direction. direction. In In the the design, design, for for the the swaying swaying and and pitch, pitch, the the controller controller uses uses the the minimum minimum laterallateral accelerationacceleration and and thethe minimumminimum rollingrolling angle angle as as thethe controlcontrol target,target, whilewhile thethe verticalvertical vibrationvibration controlcontrol uses uses the the vertical vertical acceleration acceleration as as the the control control target. target.

Appl. Sci. 2019, 9, 3326 12 of 28

Based on previous research, Ning et al. (2018) [61] proposed active vibration control of a multi-degree-of-freedom (multi-DOF) seating system with a double-layer structure (Figure 15) and an associated controller. A second layer was added to the preceding scissor-like structure to control the vibration of pitching and rocking. The second layer consists of a universal joint and four support springs. The universal joint connects two conventional rotating motors and a gearbox to generate a torque of 52 Nm to remove the rolling and pitching vibrations. The spring system is responsible for supporting the static load. This is a simple and efficient active vibration control of a multi-DOF seating system compared with other active vibration controls of multi-DOF seating systems. For the control system, a sliding mode controller to reduce the roll vibration was designed. This algorithm has the advantages of fast convergence and good robustness and can be used to control the vibration in the swinging direction. In the design, for the swaying and pitch, the controller uses the minimum lateral acceleration and the minimum rolling angle as the control target, while the vertical vibration control uses theAppl. vertical Sci. 2019, acceleration9, x FOR PEER REVIEW as the control target. 13 of 30

(a)

(b)

FigureFigure 15. (a )15. The (a) double-layer The double-layer seat suspension seat suspension prototype prototype with multi-DOF with multi-DOF vibration vibration control mechanismcontrol (b) Themechanism universal (b) joint The [ 61universal]. joint [61].

In anotherIn another project, project, with with the the same same double-layer double-layer activeactive seat suspension, suspension, an an associated associated motion motion controller was designed by Ning et al. (2018) [62] to reduce the WBV of commercial vehicle drivers. controller was designed by Ning et al. (2018) [62] to reduce the WBV of commercial vehicle drivers. The proposed active seat suspension can attenuate vibrations in 5-DOF, except the yaw vibration. The proposed active seat suspension can attenuate vibrations in 5-DOF, except the yaw vibration. The experimental results showed that when the seating system is fully controlled, the WBV can be The experimentalreduced over 40%. results showed that when the seating system is fully controlled, the WBV can be reduced overAn active 40%. seating system vibration control (Figure 16) using an electromagnetic linear actuator Anand active a related seating controller system was vibration designed control by Gan (Figure et al. 16(2015)) using [63]. an The electromagnetic system uses a linearcombination actuator of and a relatedactive controller and passive was suspensions, designed by where Gan et the al. passive (2015) su [63spension]. The system is primarily uses resp a combinationonsible for static of active load and passivebearing, suspensions, while two where electromagnetic the passive suspensionactive suspensi is primarilyons (XTA-3806) responsible that can for produce static load a peak bearing, force of while 1116 N are placed at both ends of the seat to generate the force needed to reduce vibration. In the next step, an adaptive controller (Figure 17) based on the traditional filtered-X least mean square (FXLMS) algorithm combined with an on-line fast-block LMS identification method was developed. For this project, the conventional FXLMS method was used to deal with the time-varying and nonlinear nature of the system, and the narrowband feed-forward FXLMS algorithm was employed to reduce the narrowband vibration caused by mechanical equipment. Finally, an online identification using a Fast-block least-mean-square (FBLMS) controller was used to mitigate low-frequency periodic vibrations, and multiple two-weighted adaptive filters were used in the system to reduce multiple harmonics.

Appl. Sci. 2019, 9, 3326 13 of 28 two electromagnetic active suspensions (XTA-3806) that can produce a peak force of 1116 N are placed at both ends of the seat to generate the force needed to reduce vibration. In the next step, an adaptive controller (Figure 17) based on the traditional filtered-X least mean square (FXLMS) algorithm combined with an on-line fast-block LMS identification method was developed. For this project, the conventional FXLMS method was used to deal with the time-varying and nonlinear nature of the system, and the narrowband feed-forward FXLMS algorithm was employed to reduce the narrowband vibration caused by mechanical equipment. Finally, an online identification using a Fast-block least-mean-square

(FBLMS)Appl. controller Sci. 2019, 9, x was FORused PEER REVIEW to mitigate low-frequency periodic vibrations, and multiple two-weighted14 of 30 adaptiveAppl. Sci. filters 2019, were9, x FOR used PEER inREVIEW the system to reduce multiple harmonics. 14 of 30

Figure 16. The model of the active seating system [63]. FigureFigure 16. 16.The The modelmodel of the the active active seating seating system system [63]. [63 ].

(a) (a)

(b) (b) FigureFigure 17. (a )17. The (a) block The block diagrams diagrams of the of filtered-X the filtered-X least leas meant mean square square (FXLMS) (FXLMS) controller controller (b )(b) Fast-block Fast- Figure 17. (a) The block diagrams of the filtered-X least mean square (FXLMS) controller (b) Fast- least-mean-squareblock least-mean-square FBLMS controller FBLMS controller [63]. [63]. block least-mean-square FBLMS controller [63]. Frechin et al. (2004) [64] developed an active vibration control seating system and the related displacementFrechin et al.compensation (2004) [64] developedcontroller. Thean active seat is vibration mounted control on a hemispherical seating system motion and thebase related (Figure displacement18) to attenuate compensation the 4-DOF controller.vibration control The seat through is mounted the active on a hemisphericalseat suspension. motion The acceleration base (Figure on 18)each to attenuate axis is measured the 4-DOF by vibrationa set of acceleration control through sensors the mounted active seat on suspension. the seat, and The the acceleration displacement on in eachthe axiscorresponding is measured direction by a set ofis calculatedacceleration by sensors the controller mounted (Figure on the 19). seat, The and controller the displacement then sends in a thesignal corresponding to drive thedirection actuator is calculated for displacement by the controller compensation (Figure to19). eliminate The controller vibration. then Thesends final a signal to drive the actuator for displacement compensation to eliminate vibration. The final

Appl. Sci. 2019, 9, 3326 14 of 28

Frechin et al. (2004) [64] developed an active vibration control seating system and the related displacement compensation controller. The seat is mounted on a hemispherical motion base (Figure 18) to attenuate the 4-DOF vibration control through the active seat suspension. The acceleration on each axis isAppl. measured Sci. 2019, 9, byx FOR a setPEER of REVIEW acceleration sensors mounted on the seat, and the displacement15 of 30 in the corresponding direction is calculated by the controller (Figure 19). The controller then sends a signal to experimentalAppl. Sci. 2019 results, 9, x FOR proved PEER REVIEW that this active seating system could reduce low-frequency vibration15 of 30 (2– drive6 the Hz) actuator and improve for displacement the comfort of compensation drivers [64]. to eliminate vibration. The final experimental results proved thatexperimental this active results seating proved system that this could active reduce seating low-frequencysystem could reduce vibration low-frequency (2–6 Hz)vibration and (2– improve the comfort of6 driversHz) and improve [64]. the comfort of drivers [64].

FigureFigureFigure 18. 18. 18.The The The semispherical semisphericalsemispherical motion motion motion base base base [64]. [64]. [64 ].

Figure 19. Control algorithm design [64].

The actuators used in the prototypes are summarized in Table 1. FigureFigure 19. 19.Control Control algorithmalgorithm design design [64]. [64 ].

Table 1. Summary of the actuators of active seat suspension systems. The actuatorsThe actuators used used in thein the prototypes prototypes are are summarized summarized in in Table Table 1.1 . Degree- of- Table 1. Summary of the actuatorsFreedo of active seat suspensionWork systems. AuthorTable 1. Summary Actuator and of Driver the actuators of activeMax Output seat suspension systems.Pros and Cons m Load Degree- Actuator and Degree-of-Freedom(DOF) Max Work Author of- Pros and Cons Driver (DOF) ControlControl Output Load Freedo Work Pros Author Actuator and Driver Max Output Pros and Cons Pneumatic spring m Load Simple structurePros Proportional Vertical The characteristicsSimple structure of the Stein (1997) (DOF) Pneumatic springelectropneumatic Control1-DOF Thepneumatic characteristics spring itself of thehelp pneumatic transducer to reduce vibration Proportional Vertical spring itself help toPros reduce vibration Stein (1997) Cons electropneumaticPneumatic spring 1-DOF SimpleCons structure transducer The pneumatic structure responds Proportional Vertical The characteristics of the Stein (1997) slowly electropneumatic 1-DOF pneumatic spring itself help Low control accuracy transducer to reduce vibration Cons

Appl. Sci. 2019, 9, 3326 15 of 28

Table 1. Cont.

Actuator and Degree-of-Freedom Max Work Author Pros and Cons Driver (DOF) Control Output Load Pros Common structure Traditional shock absorbers reduce Maciejewski et al. Vertical 55 kg energy consumption Pneumatic spring (2014) 1-DOF 98 kg Cons Slow dynamic response Need pipeline and compressor Pros Common structure Traditional shock absorbers reduce 50 kg Maciejewski et al. Vertical energy consumption Pneumatic spring 80 kg (2010) 1-DOF Cons 120 kg Slow dynamic response Need pipeline and compressor Pros Common structure Traditional shock absorbers reduce Maciejewski Vertical Force = 51 kg Pneumatic spring energy consumption (2012) 1-DOF 400 N 102 kg Cons Slow dynamic response Need pipeline and compressor Pros Simple structure 400 W Panasonic Torque Vertical Responsive Ning et al. (2016) servo motors 2 = 104 80 kg × 1-DOF Easy to control (MSMJ042G1U) 2 Nm × Con Bulky Pros 400 W Panasonic Simple structure servo motors Torque Vertical 55 kg Responsive Ning et al. (2017) servo motor = 26 1-DOF 70 kg Easy to control drivers Nm Con (MBDKT2510CA1) Bulky Pros 400 W Panasonic Simple structure servo motors Vertical Responsive Ning et al. (2017) servo motor 1-DOF Easy to control drivers Con (MBDKT2510CA1) Bulky Pros 400 W Panasonic Simple structure servo motors Torque Vertical Responsive Ning et al. (2016) servo motor = 52 55 kg 1-DOF Easy to control drivers Nm Con (MBDKT2510CA1) Bulky 400 W Panasonic Pros servo motors 4 Torque × Simple structure = 52 Vertical and roll Responsive Ning et al. (2018) servo motor Nm 80 kg 2-DOF Easy to control drivers Force = Con (MBDKT2510CA1) 350 N Bulky 4 × Pros Stein and Ballo Vertical High output power Electrohydraulic 75 kg (1991) 1-DOF High control precision ConBulky Pros Simple structure Electromagnetic Vertical Force = Responsive Gan et al. (2015) linear actuator 55 kg 1-DOF 1116 N Easy to control (XTA-3086) 2 × Con High power consumption Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 30

Pros Simple structure Electromagnetic linear Vertical Force = 1116 Responsive Gan et al. (2015) 55 kg actuator (XTA-3086) ×2 1-DOF N Easy to control Con High power consumption

Appl. Sci. 2019, 9, 3326 16 of 28 2.2. Simulation

2.2.In the Simulation study of active seat suspension of vehicles, in addition to using a prototype to verify the control algorithm, conducting simulations with professional software is also a common method to In the study of active seat suspension of vehicles, in addition to using a prototype to verify the verifycontrol the feasibility algorithm, of conducting the controller simulations design. with professional software is also a common method to verifyDu et the al. feasibility (2013) [65] of the proposed controller a design.model combining automotive chassis suspension, active seat suspension,Du etand al. the (2013) driver’s [65] proposed body to analyze a model and combining achieve automotive integrated chassis vibration suspension, control. activeA static seat output feedbacksuspension, controller and the considering driver’s body driver to analyze weight and changes achieve and integrated actuator vibration saturation control. was A staticdesigned output for an activefeedback seating controller system. The considering simulation driver results weight showed changes that and this actuator integrated saturation control was strategy designed for for an an active seat activesuspension seating system system. could The simulation improve results comfort showed and robustness. that this integrated control strategy for an active seatDifferent suspension controllers system couldare always improve compared comfort and to find robustness. out the most suitable control method for the active vibrationDifferent control controllers of the are alwaysseating compared system. Wang to find and out theKazmierski most suitable (2005) control [66] methoddeveloped for the a Very Highactive Speed vibration Integrated control Circuit of the seating Hardware system. Description Wang and Kazmierski Language (2005) that [Includes66] developed Analog a Very and High Mixed- Speed Integrated Circuit Hardware Description Language that Includes Analog and Mixed-Signal Signal Extensions (VHDL-AMS) model for vibration control of a car seating system, including an Extensions (VHDL-AMS) model for vibration control of a car seating system, including an active active electromechanical actuator. In that study, five different controllers were compared and electromechanical actuator. In that study, five different controllers were compared and analyzed, and analyzed,the optimal and controlthe optimal (OC) algorithmcontrol (OC) was identifiedalgorithm as wa providings identified the best as resultsproviding for vibration the best control. results for vibrationIn another control. study, In Al-Junaid another et study, al. (2006) Al-Junaid [67] proposed et al. a (2006) seating [67] system proposed model (Figure a seating 20) under system active model (Figurevibration 20) under control active by the vibration SystemC-A control modelling by technique.the SystemC-A Four control modelling algorithms technique. were compared Four control algorithmswith the were OC algorithm compared for with vibration the OC mitigation algorithm performance. for vibration mitigation performance.

Figure 20. The schematic diagram of the model used in SystemC-A [67]. Figure 20. The schematic diagram of the model used in SystemC-A [67]. Wang et al. (2018) [68] proposed a parameter identification method to identify the system parametersWang et ofal. a(2018) 5-DOF discrete[68] proposed spring–mass–damper a parameter seating identification system model method of a truck-basedto identify on the truck system parametersfield test of data. a 5-DOF The parameter discrete identificationspring–mass–damper method is basedseating on system trial and model error to of match a truck-based the measured on truck fieldnatural test data. resonant The parameter frequencies identification and vibration acceleration method is based amplitude on trial at the and selected errorfrequencies to match the with measured the naturalsimulated resonant ones frequencies of the 5-DOF and discrete vibration spring–mass–damper acceleration seatingamplitude system at model.the selected The disadvantage frequencies of with the trial and error method is the inefficient parameter identification process, which requires much time the simulated ones of the 5-DOF discrete spring–mass–damper seating system model. The and effort. The 5-DOF discrete spring–mass–damper seating system model can be used to simulate the disadvantage of the trial and error method is the inefficient parameter identification process, which vibration response of the human body. A sensitivity analysis was conducted using the Monte Carlo requiresmethod much based time on theand 5-DOF effort. model. The 5-DOF In that paper, discrete primary spring–mass–damper and secondary PID controllersseating system were applied model can be usedto the to seating simulate system the forvibratio activen vibration response control of the (Figure human 21 ).body. The secondary A sensitivity PID controlleranalysis was produces conducted a desired output control force signal according to the acceleration feedback signal of the mass oscillator. Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 30

using the Monte Carlo method based on the 5-DOF model. In that paper, primary and secondary PID Appl. Sci. 2019, 9, 3326 17 of 28 controllers were applied to the seating system for active vibration control (Figure 21). The secondary PID controller produces a desired output control force signal according to the acceleration feedback Thesignal primary of the PID mass controller oscillator. uses The the primary relative PID displacement controller feedbackuses the signalrelative and displacement the signal generated feedback fromsignal the and secondary the signal PID generated controller from as input the secondary error signals. PID Then, controller the primary as input PID error controller signals. generates Then, the a finalprimary output PID control controller force generates signal to a drive final theoutput actuator contro tol provideforce signal control to drive to mitigate the actuator vibrations to provide of the seatingcontrol system.to mitigate The advantagesvibrations of of the PIDseating control syst fromem. The that advantages research are of that the it isPID simple control and from practical, that butresearch it also are has that the it disadvantage is simple and of practical, poor robustness. but it also has the disadvantage of poor robustness.

FigureFigure 21.21. BlockBlock diagramdiagram ofof thethe proportional-integral-derivativeproportional-integral-derivative (PID)(PID) controlcontrol systemsystem [[68].68].

AnotherAnother PIDPID controlcontrol algorithmalgorithm applicationapplication forfor activeactive vibrationvibration controlcontrol ofof seatingseating systemssystems waswas proposedproposed byby AliAli etet al.al. (2019)(2019) [[69].69]. In that study,study, aa 13-DOF13-DOF biodynamicbiodynamic modelmodel waswas designeddesigned forfor improvingimproving comfort comfort and and reducing reducing the the effects effects of low-frequency of low-frequency vibration vibration on pregnant on pregnant women women and fetuses. and Forfetuses. the controllerFor the controller design, design, the genetic the genetic algorithm algorithm was used wasto used optimize to optimize the PID the coePIDffi coefficientscients and and the performancethe performance of the of PIDthe controller.PID controller. In addition, In addition the, fuzzy the fuzzy PID (FigurePID (Figure 22) control 22) control algorithm algorithm was alsowas usedalso used to design to design the controller the controller to improve to improve the robustness the robustness of the vibrationof the vibration control ofcontrol the seating of the system.seating Thesystem. performances The performances of these twoof these different two PIDdifferent controllers PID controllers were compared were compared through simulation through simulation with that ofwith a seating that of system a seating with system a passive with suspension. a passive The suspension. results proved The thatresults the proved fuzzy PID that controller the fuzzy in thatPID studycontroller was thein that best study for the was low-frequency the best for the vibration low-frequency control of vibration the seating control system. of the seating system.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 30 Appl. Sci. 2019, 9, 3326 18 of 28 Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 30

Figure 22. Fuzzy logic controller block [69]. Figure 22. Fuzzy logic controller block [69]. A feed-forward adaptiveFigure controller 22. Fuzzy (Figure logic 23 controller) combined block [with69]. the FXLMS algorithm and a feedback controller combined with the H-infinity algorithm was proposed by Wu and Chen (2004) AA feed-forward feed-forward adaptive adaptive controller controller (Figure (Figure 2323)) combined combined with with the the FXLMS FXLMS algorithm algorithm and and a a [70] to reduce the small-amplitude vibration of a vehicle seating system. The researchers compared feedbackfeedback controller combinedcombined withwith the the H-infinity H-infinity algorithm algorithm was was proposed proposed by by Wu Wu and and Chen Chen (2004) (2004) [70] three different control methods and found that the FXLMS feed-forward adaptive controller alone [70]to reduce to reduce the small-amplitudethe small-amplitude vibration vibration of a vehicleof a vehicle seating seating system. system. The researchersThe researchers compared compared three could reduce the vibration amplitude of 4.8 dB at 10 Hz when it was turned on compared with the threedifferent different control control methods methods and found and found that the that FXLMS the FXLMS feed-forward feed-forward adaptive adaptive controller controller alone alone could situation when the controller was turned off. The feedback controller with the H-infinity algorithm couldreduce reduce the vibration the vibration amplitude amplitude of 4.8 dBof at4.8 10 dB Hz at when 10 Hz it when was turned it was onturned compared on compared with the with situation the could reduce the vibration amplitude of 3.6 dB. Finally, the hybrid controller combined with the two situationwhen the when controller the controller was turned was off .turned The feedback off. The controller feedback with controller the H-infinity with the algorithm H-infinity could algorithm reduce control methods successfully reduced the vibration amplitude of 11 dB. The possible reason for this couldthe vibration reduce amplitudethe vibration of 3.6amplitude dB. Finally, of 3.6 the dB. hybrid Finally, controller the hybrid combined controller with thecombined two control with methodsthe two may have been that although the feed-forward adaptive FXLMS algorithm has the advantage of controlsuccessfully methods reduced successfully the vibration reduced amplitude the vibration of 11 dB. amplitude The possible of 11 reason dB. The for possible this may reason have been for this that taking less computational time and easy implementation, it relies on the measurable reference signal. mayalthough have thebeen feed-forward that although adaptive the feed-forward FXLMS algorithm adaptive has theFXLMS advantage algorithm of taking has lessthe computationaladvantage of Once an unpredictable change of the vibration source occurs, this change greatly affects the takingtime and less easy computational implementation, time and it relies easy implementation, on the measurable it relies reference on the signal. measurable Once anreference unpredictable signal. convergence speed of the adaptive feed-forward controller algorithm. However, the feedback Oncechange an of unpredictable the vibration source change occurs, of the this vibration change greatly source aff ectsoccurs, the convergencethis change speedgreatly of theaffects adaptive the controller based on the H-infinity algorithm has good and robust performance. Therefore, the hybrid convergencefeed-forward speed controller of algorithm.the adaptive However, feed-forward the feedback controller controller algorithm. based on However, the H-infinity the algorithmfeedback controller combining the advantages of the two algorithms should have the best and most robust controllerhas good andbased robust on the performance. H-infinity algorithm Therefore, has the good hybrid and controllerrobust performance. combining Therefore, the advantages the hybrid of the performance for seat vibration control. controllertwo algorithms combining should the have advantages the best andof the most two robust algorithms performance should for have seat the vibration best and control. most robust performance for seat vibration control.

Figure 23. Schematic of the hybrid controller [70]. Figure 23. Schematic of the hybrid controller [70]. A feedback controller basedFigure on23. theSchematic H-infinity of the algorithm hybrid controller was proposed [70]. by Sun et al. (2011) [71] A feedback controller based on the H-infinity algorithm was proposed by Sun et al. (2011) [71] to reduce the vibration transmissibility of a seating system. A 3-DOF mass–spring biological model to reduce the vibration transmissibility of a seating system. A 3-DOF mass–spring biological model (FigureA feedback 24) was controller introduced based to simulate on the H-infinity the human algo bodyrithm to improvewas proposed the accuracy by Sun ofet al. the (2011) controller. [71] (Figure 24) was introduced to simulate the human body to improve the accuracy of the controller. toUnlike reduce the the traditional vibration H-infinity transmissibility algorithm of a controller, seating system. the controller A 3-DOF in this mass–spring paper was biological optimized model with a Unlike the traditional H-infinity algorithm controller, the controller in this paper was optimized with (Figuretargeted 24) frequency was introduced range by to using simu thelate generalized the human Kalman–Yakubovich–Popov body to improve the accuracy (KYP) of lemma, the controller. which is a targeted frequency range by using the generalized Kalman–Yakubovich–Popov (KYP) lemma, Unlikemainly the controlled traditional in the H-infinity sensitive algorithm frequency controller, range of human the controller body (4–8 in this Hz). paper According was optimized to the ISO with 2631 which is mainly controlled in the sensitive frequency range of human body (4–8 Hz). According to astandard, targeted thefrequency D-class roadrange profile by using roughness the gene wasralized simulated Kalman–Yakubovich–Popov as the road surface excitation (KYP) for the lemma, active the ISO 2631 standard, the D-class road profile roughness was simulated as the road surface excitation whichsuspension is mainly system. controlled The simulation in the sensitive results showed frequency that range the system of human could body significantly (4–8 Hz). reduce According vibration to for the active suspension system. The simulation results showed that the system could significantly thein theISO targeted 2631 standard, range (4–8 the D-class Hz). road profile roughness was simulated as the road surface excitation forreduce the active vibration suspension in the targeted system. range The simulation (4–8 Hz). results showed that the system could significantly reduce vibration in the targeted range (4–8 Hz).

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FigureFigure 24. 24. Three-DOF Three-DOF biodynamic biodynamic model model [71]. [[71].71].

DuDu et et al. al. (2012) (2012) (2012) [72] [72[72]] proposedproposed an an integratedintegrated control control strategy strategy that thatcombines combines the driver the driverbody body biodynamicbiodynamic model model and and thethe quarterquarter car car model model to to include include active active seat seat suspension suspension and active and activevehicle vehicle suspension vibration controls to enhance ride comfort (Figure 25). In the controller design, the state suspension vibration controls to enhance ride comfortcomfort (Figure 2525).). In the controller design, the state feedback H-infinity controller of model simplification provides good robustness, considering friction feedback H-infinityH-infinity controller of model simplification simplification provides good robustness, considering friction as a feedback signal. According to the experiment results, the system can largely reduce the driver’s as a feedback signal. According to the experiment results, the system can largely reduce the driver’s as ahead feedback acceleration. signal. ItAccording was shown to thethat experimentintegrated active results, seat the suspension system can and largely vehicle reduce suspension the driver’s head acceleration. It was shown that integrated active seat suspension and vehicle suspension controls headcontrols acceleration. could improve It was comfort shown compared that integrated with other activeseparate seat controls. suspension and vehicle suspension couldcontrols improve could improve comfort comparedcomfort compared with other with separate other controls.separate controls.

Figure 25. The model of the integral control strategy [72].

Figure 25. The model of the integral control strategy [[72].72].

The different control system design details are summarized in Table2.

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Table 2. Summary of the control system designs.

Vibration Control Performance Author Method Target Frequency Model Performance Pros and Cons Criterion Pros Simple The peaks of vibration amplitude are Wang et al. (2018) PID control 7.51 Hz Displacement (m) 5-DOF Practical reduced to around 5 10 10 m × − Con Poor robustness Seat Effective Amplitude Pros Adaptive control Transmissibility (SEAT) Adaptable Maciejewski et al. Reverse model The vibrations at about 1.3 Hz are 0.5–4 Hz (dimensionless) 1-DOF Large range of load (2014) Proportional-Derivative reduced by about 50% Transmissibility (Dimensionless) Con (PD) control Acceleration (m/s2) Signal delay Pro Robust control Can work with a different Maciejewski et al. SEAT (Dimensionless) The amplitude at resonance is reduced Triple feedback 0.5–4 Hz mass load (2010) Transmissibility (Dimensionless) by about 50% control Con May cause chattering Pro FXLMS control The active seating system achieves 11 Good robustness Wu and Chen (2004) 10, 20, and 30 Hz Accelerations in dB ref 1 m/s2 H-infinity control dB vibration attenuation at 10 Hz Con May cause chattering Root-mean-square (RMS) Compared with a passive seating acceleration (m/s2) system, Pro Frequency-weighted RMS H-infinity control the RMS is reduced by 57%, the Good robustness (FW-RMS) acceleration (m/s2) Ning et al. (2016) with friction 1–4.5 Hz 1-DOF FW-RMS is reduced by 35.5%, the Con Vibration Dose Value (VDV) compensation VDV value is reduced by 34.6%, the Highly reliant on the (m/s1.75) SEAT value is reduced 35.6%, and the accuracy of the model SEAT (dimensionless) VDV ratio is reduced by 34.6%. VDV ratio (dimensionless) Pro H-infinity control in Power Spectral Density (PSD) Good robustness Sun et al. (2011) the finite frequency 4–8 Hz (m2/s3) 3-DOF Con domain Acceleration (m/s2) Highly reliant on the accuracy of the model For single-frequency cancellation, a 26 dB cancellation is achieved on the seat Pro pan at the frequency of 6 Hz. Adaptable FXLMS control Gan et al. (2015) 4–12 Hz dB ref m/s2 For the multiple harmonic Con FBLMS control cancellation, the average level of Can be affected by noise or vibration reduction is around 20 dB at disturbance 4, 6, 8, and 12 Hz. Appl. Sci. 2019, 9, 3326 21 of 28

Table 2. Cont.

Vibration Control Performance Author Method Target Frequency Model Performance Pros and Cons Criterion RMS acceleration (m/s2) Compared with a passive seating Pro FW-RMS acceleration (m/s2) system, the RMS is reduced by 31.96%, Good robustness VDV (m/s1.75) the FW-RMS is reduced by 43.42%, the Ning et al. (2016) H-infinity control 2–6 Hz 2-DOF Con SEAT (Dimensionless) VDV value is reduced by 42.96%, the Highly reliant on the VDV ratio SEAT value is reduced 43.41%, and the accuracy of the model (Dimensionless) VDV ratio is reduced by 42.68%. Compared with a passive seating system, for vertical acceleration, the Pros RMS is reduced by 41.9%, the FW-RMS Adaptable RMS (m/s2) is reduced by 32.1%, and the VDV is Sliding mode control High robustness Ning et al. (2018) FW-RMS (m/s2) 2-DOF reduced by 32.8%. H-infinity control Con VDV (m/s1.75) For lateral acceleration cancellation, Highly reliant on the the RMS is reduced by 55.4%, the accuracy of the model FW-RMS is reduced by 49.4%, and the VDV is reduced by 52.2%. Compared with a well-tuned passive Disturbance observer seating system, the active seating Pro Acceleration (m/s2) Ning et al. (2017) Takagi–Sugeno fuzzy 2–4 Hz 2-DOF system can reduce the RMS by 45.5% Effectively reduce the Transmissibility (Dimensionless) control and 49.5% with a mass load of 55 and workload 70 kg, respectively. Pro Good robustness H-infinity state Du et al. (2012) Head acceleration (m/s2) 8-DOF Con feedback control Highly reliant on the accuracy of the model Compared with a passive seating RMS acceleration Pros system, (m/s2) Adaptable the RMS is reduced by 54.6%, the FW-RMS acceleration (m/s2) High robustness Ning et al. (2017) Sliding mode control 1.5 Hz 2-DOF FW-RMS is reduced by 34.1%, the VDV (m/s1.75) Con VDV value is reduced by 32.6%, the SEAT (Dimensionless) Highly reliant on the SEAT value is reduced 34.1%, and the VDV ratio (Dimensionless) accuracy of the model VDV ratio is reduced by 32.6%. Appl.Appl. Sci. Sci. 20192019, 9, 9x, FOR 3326 PEER REVIEW 24 22of of30 28

3.3. ANN ANN Control Control UnlikeUnlike a amodel-based model-based controller, controller, the the ANN ANN controller controller is isdata-based. data-based. It Itcan can be be used used to to identify identify complexcomplex nonlinear nonlinear objects objects that that are are difficult difficult to to accurately accurately model model and and can can be be used used as as a acontroller controller for for adaptiveadaptive control. control. Compared Compared with with traditional traditional controllers, controllers, ANN ANN controllers controllers have have many many features. features. They They havehave powerful powerful nonlinear nonlinear processing processing capabilities capabilities and and are are well well suited suited for for dealing dealing with with problems problems with with a alarge large number number of of input input variables, variables, as as well well as as multivariable multivariable output. output. In In addition, addition, the the ANN ANN can can learn learn unknownunknown information information autonomously. autonomously. However, However, for ac fortive active vibration vibration control control of a seating of a seating system, system, the ANNthe ANNsystem system has not has been not beenwidely widely accepted. accepted. AnAn ANN ANN controller controller (Figure(Figure 26 26)) was was proposed proposed by Gucluby Guclu and Gulezand Gulez (2008) (2008) [73] to [73] control to acontrol nonlinear a nonlinearvehicle model vehicle having model 8 DOF.having In this8 DOF. study, In thethis active study, seat the suspension active seat systemsuspension control system was combined control was with combinedan active with vehicle an suspensionactive vehicle system suspension control. sy Thestem ANN control. controller The couldANN solvecontroller the nonlinear could solve problem the nonlinearcaused by problem the vehicle caused system by the under vehicle excitation system disturbances. under excitation The disturbances. ANN model wasThe trainedANN model with errorswas trainedbetween with actual errors and between expected actual output and results expected by ou usingtput backpropagation. results by using backpropagation. The results demonstrated The results that demonstratedthe ANN controller that the performs ANN controller well at controllingperforms well seating at controlling system vibration. seating system vibration.

FigureFigure 26. 26. AnAn integral integral controller controller based based on on artificial artificial neural neural networks networks (ANNs) (ANNs) for for active active chassis chassis suspensionsuspension and and active active seat seat susp suspensionension system system controls controls [73]. [73 ].

TheThe control control system system for for an an active active seat seat suspensi suspensionon combining combining an an ANN ANN module, module, the the active active force force controlcontrol (AFC) (AFC) method, method, and and a aPID PID controller controller (Figure (Figure 27) 27 was) was proposed proposed by by Gohari Gohari and and Tahmasebi Tahmasebi (2015)(2015) [74]. [74]. In In the designdesign of of the the ANN ANN model, model, a hidden a hidden layer layer structure structure of 10 neural of 10 unitsneural was units considered was consideredto have the to besthave performance. the best performance. Reverse learning Reverse was learning used towas train used the to ANN train modelthe ANN to estimate model to the estimatemass of the the mass seat–occupant of the seat–occupant system. In addition,system. In the addition, PID controller the PID was controller designed was to designed work with to minimal work withdisturbance minimal and disturbance at low speed, and and at thelow AFC speed, controller and the was AFC used controller to improve was the robustnessused to improve and vibration the robustnesscontrol performance and vibration of the control control performance system. In thisof th controller,e control thesystem. system In errorthis controller, was taken the as thesystem input, errorand was the estimatedtaken as massthe input, was setand as the the estimated output. The mass Levenberg–Marquardt was set as the output. algorithm The Levenberg– was used to Marquardtprovide numerical algorithm solutions was used for to nonlinear provide numerical minimization. solutions After for comparison nonlinear with minimization. the PID controller, After comparisonthe ANN control with the performed PID controller, better thanthe ANN the other control controllers. performed better than the other controllers.

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FigureFigure 27. 27. AnAn intelligent intelligent controller controller via via an an ANN ANN algorithm algorithm [74]. [74].

TheThe ANN ANN controllers controllers mentioned mentioned here here are are summarized summarized in in Table Table 33.. Table 3. Summary of ANN controllers. 4. Biodynamic Modeling Number of Number of Training InAuthor the study ofHidden the active vibration control of a vehicle seating system, an accuratePros andbiodynamic Cons Nodes Method model which aims Layersto predict the dynamic response of a human body is necessary because it can Compared with an provide more efficient control. In [68], a 5-DOF seat–occupant model (Figure 28), which Procan be used uncontrolled system, the Good on multi-input to simulate the dynamic behaviour of the human body, wasmaximum proposed displacement and the parameter sensitivity Guclu and First layer: 9 Back and multioutput was determined based2 on the model; the relevant data wereof thealso passenger used in seat the design of the active Gulez (2008) Second layer:10 Propagation control with NN control is control system. In [71], a 2-DOF biodynamic model and a 2-DOF seat suspension system Conmodel were reduced from 2.8 10 3 × − Low robustness combined, and a 3-DOF seat–occupant model was establishedto to 0.2 describe10 3 m. the dynamic response of × − the human body in space. However, due to the difficulty of measuring actual human bodyPro properties Good on nonlinear and becauseGohari and an accurate biodynamic model may dramatically increase the amount of computation Back problems Tahmasebi 1 10 time, a simplified model has been used as a commonPropagation method in research. In [59, 60], the humanCon body (2015) was replaced by a mass, and the amount of calculation time was reduced by ignoringNeed the large complex ideal dynamic behavior of the human body itself. training data

4. Biodynamic Modeling Table 3. Summary of ANN controllers. In the study of the active vibration control of a vehicle seating system, an accurate biodynamic Number of Number Training modelAuthor which aimsHidden to predict the dynamic response of a human body is necessary Pros because and Cons it can of Nodes Method provide more effiLayerscient control. In [68], a 5-DOF seat–occupant model (Figure 28), which can be used to simulate the dynamic behaviour of the human body, wasCompared proposed with and an the parameterPro sensitivity was determined based on the model; the relevant datauncontrolled were also used system, in thethe designGood of on the multi- active First control system. In [71], a 2-DOF biodynamic model and amaximum 2-DOF seat displacement suspension systeminput model and were Guclu and layer: 9 Back 2 of the passenger seat multioutput combined,Gulez (2008) and a 3-DOF seat–occupantSecond modelPropagation was established to describe the dynamic response of the human body in space. However, due to the difficulty of measuringwith NN actual control human is body propertiescontrol and layer:10 because an accurate biodynamic model may dramaticallyreduced increase from the 2.8 amount × 10 of computationCon time, to 0.2 × 10 m. Low robustness a simplified model has been used as a common method in research. In [59,60], the human body was Pro replaced by a mass, and the amount of calculation time was reduced by ignoring the complex dynamic Good on behaviorGohari and of the human body itself. nonlinear Back Tahmasebi 1 10 problems Propagation (2015) Con Need large ideal training data

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FigureFigure 28. 28.A A 5-DOF 5-DOF seat–occupant seat–occupant model model [68 [68].]. 5. Our Contributions 5. Our Contributions Our group has been working on the active vibration control of a seating system since 2016. Our group has been working on the active vibration control of a seating system since 2016. A 5- A 5-DOF seat–occupant biodynamic model was developed and applied in vibration control simulation DOF seat–occupant biodynamic model was developed and applied in vibration control simulation research [68]. A new active vibration controller for a seating system that combines a traditional rotating research [68]. A new active vibration controller for a seating system that combines a traditional and a scissor-like structure was designed and built to reduce low-frequency vibration and increase ride rotating and a scissor-like structure was designed and built to reduce low-frequency vibration and comfort [57]. The PID and H-infinity controllers were designed and applied to the active vibration increase ride comfort [57]. The PID and H-infinity controllers were designed and applied to the active control seating system in [57,68]. An analysis of the parameter sensitivity of a 5-DOF model-based vibration control seating system in [57,68]. An analysis of the parameter sensitivity of a 5-DOF model- Monte Carlo simulation was performed [68]. The measurement data and results recorded from four based Monte Carlo simulation was performed [68]. The measurement data and results recorded from actual trucks in a field test were applied in the research to identify the 5-DOF system parameters of four actual trucks in a field test were applied in the research to identify the 5-DOF system parameters the human body seating system [68]. The relevant active vibration control of the seating system was of the human body seating system [68]. The relevant active vibration control of the seating system simulated in the Simulink software [68]. was simulated in the Simulink software [68]. Identified Research Gaps, Research Questions, and New Directions 5.1. Identified Research Gaps, Research Questions, and New Directions In previous research, a number of innovations in terms of the active vibration control of a seating systemIn have previous been research, proposed. a Vibrationnumber of control innovations of a seating in terms system of the has active been vibration well studied control and of analyzed a seating insystem the laboratory. have been However, proposed. there Vibration are still control some of research a seating areas system that has could been be well improved studied orand gaps analyzed that needin the to laboratory. be filled. However, there are still some research areas that could be improved or gaps that needIn to terms be filled. of active vehicle suspension control, there are at least four active control algorithms that have beenIn terms applied of active to the vehicle active suspension vibration control control, of ther seatinge are systems.at least four However, active control the vibration algorithms control that performancehave been applied has not to been the crosswiseactive vibration compared control to these of seating various systems. controllers. However, Such athe project vibration may control allow researchersperformance to identifyhas not been which crosswise control algorithmcompared is to best these for various vibration controllers. control performance. Such a project may allow researchersDue to theto identify particularity which ofcontrol the human algorithm body, is best it is for di vibrationfficult to control identify performance. its model parameters in real-time.Due to the So, particularity a real-time parameterof the human identification body, it is difficult method to for identify human its body model biodynamic parameters model in real- parameterstime. So, a willreal-time help improveparameter controller identification performance. method for human body biodynamic model parameters willCompared help improve with controller traditional performance. controllers, ANN control reduces many of the modelling and calculation steps andCompared simplifies with controller traditional design. controllers, However, ANN ANN control training reduces requires many a large of amount the modelling of ideal data and forcalculation back-propagation steps and training. simplifies Therefore, controller determining design. However, how to use ANN an unsupervisedtraining requires learning a large algorithm amount inof place ideal of data a traditional for back-propagation supervised learning training. algorithm Therefore, is an determining important future how researchto use an direction. unsupervised learningAn ANN algorithm control in system place has of weaka traditional robustness supervised due to sudden learning and unmanageablealgorithm is an signals. important Therefore, future combiningresearch direction. the ANN system with a traditional control system, using the control results of the traditional systemAn to trainANN the control ANN controller,system has and weak enhancing robustness the robustness due to sudden of the system and unmanageable are also research signals. areas worthyTherefore, of consideration. combining the ANN system with a traditional control system, using the control results of the traditional system to train the ANN controller, and enhancing the robustness of the system are also research areas worthy of consideration.

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From the above-identified research gaps, the following research questions are raised: 1. What kind of ANN algorithm can be used to improve the PID control algorithm for active vibration control seating systems? 2. How can an unsupervised deeper learning algorithm be used to improve the performance of vibration cancellation in active seating systems? 3. How can an ANN algorithm be used to improve the robustness of a control system?

6. Conclusions This article reviewed recent research on and developments in active vibration control in seat suspension systems. The advantages and disadvantages of each of the actuators and control algorithms were discussed. Examples of ANN control technology in the active vibration control of seating systems were also illustrated. Finally, this article identified the research gaps and new research directions which have not been covered by recent works, and research questions have been raised based on the identified gaps and research directions.

Author Contributions: Y.Z. analyzed the data; Y.Z. and X.W. contributed reagents/materials/analysis tools; Y.Z. and X.W. wrote the paper. Funding: This research was funded by Australian Research Council grant number LP160100132. Conflicts of Interest: The authors declare no conflict of interest.

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