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Anti-Rollover Control System Design of Articulated Off-Road Vehicles

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Anti-Rollover Control System Design of Articulated Off-Road Vehicles MATEC Web of Conferences56, 06003 (2016 ) DOI: 10.1051/matecconf/2016 56 06003 ICCAE 2016 Anti-rollover Control System Design of Articulated Off-road Vehicles Shuai Wang1, Yuxin Zhang2 , ChaoChen ,1 and Huikai Liu 1 1College of Mechanical Science and Engineering, Jilin University, 130022 Changchun, China 2State Key Laboratory of Automotive Simulation and Control, Jilin University, 130022 Changchun, China Abstract. This paper designs an anti-rollover control system to improve articulated off-road vehicle rollover safety. This controller consists two level control which target to two different instability stages: 1) the active swing bridge adjustment control algorithm for the first stage instability; and 2) the active braking control algorithm for the second stage instability. The first level control is based on fuzzy PID control and second is based on switch control, they work together to prevent the articulated wheel loader from rollover accident. Multi-body dynamics model of a ZL50 articulated wheel loader is built by commercial software RecurDyn, and the MBD model and controller model were co-simulated under the same solver in RecurDyn/CoLink. The co-simulation results show that, during the high speed steering rollover process, these two level controller work efficiently to reduce the roll angle and improve the vehicle rollover stability. 1 Introduction The simulation results have shown that this control system can significantly improve traction semi-trailer Articulated steering system is widely used in off-road and stability, and reduce rollover accident probability. engineering vehicles due to its several advantages, such P. Gaspar developed a reconfigurable control system as small steering radius, good manoeuvring for heavy vehicles, it combined active anti-roll bar characteristics, simple structure, and low power control and active braking control [9]. At the same time, consumption. the optimal anti-rollover control strategy considered While the articulated steering vehicles became several roll dynamics parameters, such as roll angle, roll popular and widely used, the security problems are angular velocity, lateral angular velocity and lateral becoming increasingly prominent [1]. Compared to the acceleration. vehicles with overall frame, the articulated engineering or M. M. Islam built a 5 DOF single articulated trailer off-road vehicles have a special body structure, which heavy vehicle model and a 7 DOF multi-trailer heavy usually leads to an additional degree of freedom, and vehicle model, and simulated them using TruckSim [10]. reduces vehicle lateral stiffness. Therefore, this kind of By combining trailer active steering control, active anti- vehicles are easier to be caused rollover accidents which roll bar control, and differential braking control, this lead to casualties and economic loss [2]. control system can effectively improve low-speed In order to ensure the driver's life safety, international maneuverability and high-speed cornering lateral stability. standard ISO3471 requirements that the engineering N. Daher investigated a yaw stability control system for vehicles must be installed rollover protection structure articulated frame steering off-highway vehicles via novel (ROPS) which can protect the driver in rollover accidents steer-by-wire technology that they have recently passively [3], however this approach does not solve the developed [11], this work explores whether the new root of the problem. technology can also deliver increased safety as well, by At present, the safety research about articulated off- providing active safety functions. road vehicles are mainly focused on passive rollover Y. X. Zhang established wheel loader dynamic safety, rollover mechanism, rollover and steering stability rollover model and the rollover warning strategy model analysis, etc. [4]. There are only a few researches on the by MATLAB/Simulink [7]. Through the simulation active anti-rollover control system [5-7] and most of them results analysis, the effectiveness of warning strategy are focused on commercial vehicles and SUVs. under high-speed transport and sharp steering was D. J. M. Sampson et al developed an active anti- verified. What’s more, a physical prototype test was rollover control system for articulated commercial performed using the remote control loader model vehicle. vehicles, and introduced the hardware structure and In this paper, a fuzzy PID controller is designed to software development of the control system in detail [8]. improve articulated off-road vehicle rollover safety, Through controlling the active anti-roll bar by a hydraulic especially the research object is an articulated wheel actuator, it can adjust the traction semi-trailer roll motion. loader. Section 2 introduces the design process of the © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). MATEC Web of Conferences56, 06003 (2016 ) DOI: 10.1051/matecconf/2016 56 06003 ICCAE 2016 active-anti rollover controller. Section 3 describes the The traditional PID control has many advantages, but its multi-body dynamics modeling of the wheel loader. fixed set of control parameters cannot adapt to Section 4 sets up the co-simulation. Simulation results are environmental interference. shown in section 5. Finally, the conclusions are given in In order to improve the control effect, this paper section 6. developed a fuzzy PID controller, which takes advantages from both fuzzy control and traditional PID control. Based on fuzzy control, the fuzzy PID controller can tune 2 Controller Design the three key parameters KP, KI and KD of the PID control The designed active anti-rollover control system is online using fuzzy logic inference [12, 13]. divided into two levels, they are used in different stages As shown in Figure 1, a two dimensional fuzzy of the rollover process: 1) for the first stage instability of controller is used in this paper. The inputs are deviation e the loader, the active swing bridge adjustment control and its rate ec, and the outputs are the three key algorithm will be activated; and 2) for the second stage parameters of PID controller, KP, KI and KD. In practice, instability of the loader, the active braking control in order to accelerate the control system response speed, algorithm will be activated. the increment of these three parameters ˂KP, ˂KI and ˂ Fuzzy control has good noise suppression ability and KD are used instead of directly tuning parameters KP, KI good robustness, which is suitable for nonlinear systems. and KD. Figure 1 shows the structure diagram of the fuzzy However, the inherent weakness of fuzzy control is that PID controller. the steady state performance is poor, and it is easy to produce the limit oscillations near the equilibrium point. Fuzzy Controller PID Controlled Controller Object ec Figure 1. Structure diagram of the fuzzy PID controller. For the PID controller part, its inputs KP, KI and KD fuzzy discrete domains are [−ͥ,ͥ] , [−ͦ,ͦ] , [−ͥ,ͥ] , [−ͦ,ͦ] , and are calculated from the output of fuzzy controller ˂KP, ˂ [−ͧ,ͧ]. The input quantization factors are: K and ˂K and the previous system initial value K , K I D P0 I0 ͅ =ͥ/(3 (6) and KD0, and then the control variable u(t) is generated ͅ =ͦ/͗(3 (7) and acted on the controlled object. The calculation The comparative factors of output variables are: formula of K , K and K are as follows: P I D ͅ0ͥ =∆͟+(3/ͥ (8) KP=KP0+˂KP (1) ͅ0ͦ =∆͟$(3/ͦ (9) ͅ0ͧ =∆͟(3/ͧ (10) KI=KI0+˂KI (2) The fuzzy subsets of the input and output variables KD=KD0+˂KD (3) are all seven: NB (Large negative), NM (Medium For the active swing bridge control, the feedback negative), NS (Small negative), ZO (zero), PS (Small signal y(t) is roll angle (), while the given input is a positive), PM (Medium positive), PB (Large positive). fixed roll angle. Therefore, the roll angle deviation e of The membership function of fuzzy subsets is: the fuzzy control is: 3ͯ ,͕<ͬ<͖ =͌(͎) −(ͨ) =−(ͨ) (ͬ) =ƥ (11) (4) 3ͯ ,͖<ͬ<͗ The change rate ec of the roll angle deviation is: (ͤͯe(/)) where a, b, and c are slopes of membership functions. ͙͗ = ͙͘/ͨ͘ = =−!(ͨ) (5) / Based on Mamdani’s reasoning method, the fuzzy The outputs of the fuzzy control are the correction control rules of ˂KP, ˂KI and ˂KD are shown in Table 1 parameters of the PID control ˂kp, ˂ki and ˂kd. The basic to Table 3. domains of e, ec, ˂kp, ˂ki and ˂kd are [−(3,(3], In this paper, weighted average method is used to realize the process of ambiguity resolution: [−͗(3,͗(3] , [−˂͟+(3, ˂͟+(3] , ∑x} [ (∆ )×∆ [− ͟ , ͟ ] and [− ͟ , ͟ ] ∆ = Ĝu ĉĜ ĉ ĉĜ ˂ $(3 ˂ $(3 , ˂ (3 ˂ (3 , ∑x} [ (∆ ) (12) respectively. The linguistic variables of inputs and Ĝu ĉĜ ĉ outputs are E, EC, KP, KI and KD, respectively and their 2 MATEC Web of Conferences56, 06003 (2016 ) DOI: 10.1051/matecconf/2016 56 06003 ICCAE 2016 where, (∆) =(̿) ×(̿̽). The processes of ambiguity resolution for ˂KI and ˂KD are similar. Table 1. Fuzzy control rules of ΔKP. Table 3. Fuzzy control rules of ΔKD. Table 2. Fuzzy control rules of ΔKI. Finally, the Simulink model of the fuzzy PID controller is shown in Figure 2. Figure 2. Simulink model of Fuzzy PID controller. The two level Simulink control block diagrams are triggered and provides a recovery torque to the swing given below. Figure 3 shows the active swing bridge bridge. Figure 4 shows the active braking control adjustment control algorithm, which can automatically algorithm, which is based on switch control. When the tune the output torque by the fuzzy PID control. When vehicle state detection module detects the vehicle is under the vehicle state detection module detects the vehicle is the second stage instability risk, this controller will be under the first stage instability risk, this controller will be 3 MATEC Web of Conferences56, 06003 (2016 ) DOI: 10.1051/matecconf/2016 56 06003 ICCAE 2016 triggered and output a braking force to the brake pedal.
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