Disturbance Observer and Kalman Filter Based Motion Control Realization

IEEJ Journal of Industry Applications Vol.7 No.1 pp.1–14 DOI: 10.1541/ieejjia.7.1

Review Paper

∗a) ∗ Thao Tran Phuong Member, Kiyoshi Ohishi Fellow ∗∗ ∗ Chowarit Mitsantisuk Member, Yuki Yokokura Member ∗∗∗ ∗∗∗∗ Kouhei Ohnishi Fellow, Roberto Oboe Member ∗5 Asif Sabanovic Non-member (Manuscript received Oct. 23, 2017, revised Nov. 7, 2017)

Many effective robot-manipulator control schemes using a disturbance observer have been reported in the literature in the past decades. Besides, the disturbance observer combined with the Kalman filter has attracted the attention of researchers in the field of motion control. The major advantage of a motion control system based on the Kalman filter and disturbance observer is the realization of high robustness against disturbance and parameter variations, effective noise suppression and wideband force sensing. This paper presents a survey of motion control based on the Kalman filter and disturbance observer, which have been previously introduced by the authors. Several control schemes, as well as formulations and applications of the Kalman filter and disturbance observer, are described in the paper. The performance and effectiveness of the control schemes are evaluated to give a useful and comprehensive design of the Kalman filter and disturbance observer in various motion control applications.

Keywords: disturbance observer, Kalman ﬁlter, motion control, acceleration control, force control, real-world haptics

the DOB is used instead of a force sensor to estimate and 1. Introduction compensate for disturbance. Since its invention, it has be- For several decades, motion control using disturbance ob- come one of the most preferred solutions in motion control. server (DOB) has been extensively reported in the liter- By using the DOB, the disturbance force is rejected, and ro- ature and employed for various applications for the con- bust motion control is attained. In fact, the performance of trol of industrial robotics, human-robot interaction, machine a force control system is determined by the fidelity of force tools, bilateral control, motion copying system and robotic detection. This leads to a large number of elegant approaches surgery (1)–(5). Conventionally, in a force control system, force to develop the DOB as well as to improve its performance. measurement is realized using force sensors. Although com- Xiong and Saif have proposed a state functional disturbance mercial force sensors benefit from their accuracy, flexibility, observer to estimate disturbances with a bounded error (9).In and reliability, the use of force sensors in a force control sys- this method, the disturbance observer state does not precisely tem has its own noteworthy downsides. Most force sensors track the system states, which is an advantage that simplifies are not very durable and are vulnerable to impact. Besides, the estimation error dynamics. To provide satisfactory con- high-precision force sensors are not economical for limited trol performance, Chen et al. have proposed a new nonlinear budget projects. Moreover, force sensors are susceptible to disturbance observer scheme and applied it to a robotic sys- noise effect, which results in a narrow force-sensing band- tem (10). Natori et al. have introduced a time delay compensa- width, and are not suitable for diverse environments or ap- tion method based on the concept of network disturbance and plications. These drawbacks limit the use of force sensors. communication disturbance observer for network-based con- Therefore, many approaches have been proposed to develop trol systems (11). A generalized disturbance observer has been suitable force observers. One popular force-sensor-less ob- proposed by Kim et al. to estimate higher order disturbances server method is the disturbance observer (6)–(8). Essentially, in the time series expansion (12). Oh and Kong have proposed a) Correspondence to: Thao Tran Phuong. E-mail: thaotp@vos. the disturbance observer and feed-forward controller as the nagaokaut.ac.jp model-based control algorithm to achieve the high-precision ∗ Nagaoka University of Technology force control of a series elastic actuator (13). 1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan Although the DOB has superior performance in terms of ∗∗ Kasetsart University robustness, its force sensing performance deteriorates with Thailand ∗∗∗ noise. The effect of noise limits the force sensing band- Keio University 3-14-1, Hiyoshi, Kohoku-ku, Yokohamak, Kanagawa 223- width and causes oscillations in control systems. Recently, 8522, Japan the Kalman filter, a powerful tool for estimating the state ∗∗∗∗ University of Padova of a process by minimizing the mean of the squared error, Italy has been widely used in motion control applications (14)–(26). ∗5 International University of Sarajevo The noise of the measurement system and the estimations is Bosnia and Herzegovina

c 2018 The Institute of Electrical Engineers of Japan. 1 Disturbance Observer and Kalman Filter Based Motion Control（Thao Tran Phuong et al.） reduced effectively using the Kalman filter. Therefore, the approach of combining the DOB and Kalman filter in motion control systems has risen to prominence due to its advantages in achieving high robustness against disturbance and parameter variations, effective noise suppression and wideband force sensing. This paper presents a survey on motion control based on the Kalman filter and the DOB, introduced by the authors in previous works. Several control schemes, as well as formulations and applications of the Kalman filter and the DOB are described in the paper. The performance and effectiveness of the control schemes are evaluated to develop a useful and comprehensive design of the Kalman filter and the DOB in various motion control applications. Fig. 1. Recursive adaptive Kalman filter algorithm The content of the paper is organized as follows: Section 2 gives a description of the Kalman filtering algorithm. Sec- the corresponding time step, corrupted by noise. The current tion 3 presents the force-sensor-less observer approaches pro- state and the estimation error are projected forward in time posed in our previous works. These observers are designed by the predictor equations as described in (5) and (6). for disturbance torque or load torque estimation, and are x(k|k−1) = Ax(k−1|k−1) + Bu(k−1) ··················· (5) combined with the Kalman filter in motion control schemes. T P | − = AP − | − A + Q ···················· (6) Section 4 introduces the methods to integrate the Kalman fil- (k k 1) (k 1 k 1) ter and the DOB in specific applications of motion control. where P is the estimate error covariance matrix derived from Section 5 presents the summary and conclusions of the paper. the variance between the predicted state estimation and the improved state estimation. The Kalman filter gain matrix K, 2. Kalman Filtering Algorithm which is based on the total uncertainty measurement, is com- The Kalman filter (27) is an optimal estimator that minimizes puted to minimize the error covariance using the following the mean of the squared error to obtain approximations of equation. T T −1 the true response using information from a model and avail- K(k) = P(k|k−1) H (HP(k|k−1) H + R) ············(7) able measurements. The Kalman filter estimates the state of The next step is to update the prior state estimation and the a measuring process formulated in the following equations. prior error covariance. The actual measurement z(k) at every x(k+1) = Ax(k) + Bu(k) + w(k) ····················· (1) sampling time is incorporated into the previous estimations z = Hx + v ······························· (2) to obtain improved estimates using the corrector equations as (k) (k) (k) follows. Here, x(k) is the state; z(k) is the measured data corrupted x(k|k) = x(k|k−1) + K(k)(z(k) − Hx(k|k−1)) ············ (8) by noise; and u(k) is the known input to the system. The ran- P | = P | − − K HP | − ·················· (9) dom variables w(k) and v(k) represent the process noise and (k k) (k k 1) (k) (k k 1) measurement noise, respectively. k is the sampling time in- 3. Force-sensor-less Observer Approaches dex corresponding to each sampling step of the discrete measuring process. A, B and H are the state, control input and This section presents the approaches for the synthesis observation matrices, respectively. of the force-sensor-less observer proposed in our previous In (1) and (2), both w(k) and v(k) are assumed to be uncor- works. These observers are designed for disturbance force related zero-mean Gaussian white noise with the covariance estimation or load force estimation, and have been integrated matrix of process noise, Q, and the covariance matrix of mea- with the Kalman filter in various motion control schemes. surement noise, R. The covariance matrices Q and R are de- 3.1 Disturbance Observer for Force Estimation (6)–(8) fined as follows. A disturbance observer is a technique for the estima- Q = E wwT ·································· (3) tion and compensation of the disturbance force without using a force sensor. A robust acceleration control is realized by R = E vvT ···································· (4) using the DOB. The DOB estimates the parameter variation, where Q and R are nonnegative definite matrices and E[] nonlinear friction, and other disturbances into consideration denotes the expected value. For a steady-state Kalman filter as one state variable. in which the noise characteristics are constant, Q and R can Figure 2 shows the block diagram of the conventional DOB be determined by using the testing simulations of the mea- with a motor model. Here, Jm denotes the motor inertia, Kt surable signal, which is based on actual experimental data. denotes the torque coefficient and the subscript n represents θ The susceptibility of the Kalman filter to noise depends on the nominal value. Im is the motor torque current, m is the ω the process noise covariance matrix Q and the measurement angle and m represents the motor speed. Tm denotes the noise covariance matrix R. motor torque and Tdis is the disturbance torque. The expres- Figure 1 shows the algorithm of the Kalman filter. The sion for the disturbance torque in Fig. 2 is described in many Kalman filter uses a form of feedback control to estimate the papers (28) (29). state x. The filter predicts the state for a given time step and The dynamic equation of the motor is expressed by (10). then obtains the feedback in the form of measurements for Jmω˙ m = Tm − Tdis ······························(10)

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3.2 Friction-free High-order Disturbance Observer for Force Estimation (15) The performance of a force control system is determined by the accuracy of estimating the force. However, in practical applications, force estimation by the conventional DOB is deteriorated by friction phenomena. This also degrades the performance of the control system. The elimination of friction improves force sensing and control performance. Our friction compensation method is the dithering technique, which is preferred to other tech- niques owing to its simple implementation. The effect of a dither signal is to smoothen the discontinuity due to friction. Therefore, by using the dithering method, we do not consider the friction model in our approach. However, a dither signal introduces oscillatory disturbance in force estimation by the conventional DOB. Therefore, a force-sensing method capable of eliminating the effect of the Fig. 2. Block diagram of the conventional disturbance observer oscillatory component in the force information is required. Based on this reasoning, we have proposed a high-order dis- The total generated motor torque is given by (11). turbance observer (HDOB) (15) to perform the force sensing = ····································· Tm KtIm (11) function while reducing friction and the oscillatory compo- The disturbance torque Tdis is defined as the total load torque nent in the force information. applied on the motor including the effect of parameter varia- The dynamics of the oscillatory signal is modeled by a tion and is represented as given in (12). second-order system. The plant system of the HDOB is Tdis = Text + Fc + Dωm + (Jm − Jmn)˙ωm + (Ktn − Kt)Im shown in Fig. 3. Therefore, the oscillatory torque is expressed ··················· (12) as follows. ω2 where T denotes the reaction torque, F denotes the ext c T = 0 T ·····················(17) ω dis 2 2 dis Coulomb friction and D m denotes the viscous friction. s + 2ζω0 s + ω − ω 0 (Jm Jmn)˙m is the torque due to the variation in self-inertia where ω0 is the predetermined angular frequency of the addi- − and (Ktn Kt)Im is the torque ripple caused by the variation tional periodic signal and ζ denotes the damping ratio. Here, ffi of the torque coe cient. Tdis is the torque signal without the oscillatory component, For the case where the motor parameter variations are zero, which is the step function expressed by (18); Tdis is the oscil- the disturbance force is given by (13). latory torque generated by superimposing the periodic signal = + + ω ························· Tdis Text Fc D m (13) on Tdis. From (10) and (11), the disturbance torque is expressed by d T = 0·····································(18) (14). dt dis Tˆdis = KtnIm − Jmnω˙ m ·························· (14) The model of the HDOB is realized based on the state Originally, equation (14) indicates that the disturbance space model in (19) and (20). torque is estimated based on the motor acceleration. The ac- x˙ = Ax + Bu ·································· (19) celeration is estimated based on the position using the second y = Cx ········································ (20) derivative calculation. However, the second derivative of the where ⎡ ⎤ position exhibits high noise level; therefore, a low-pass filter ω ⎢ m ⎥ is used to filter out the high-frequency noise, and the DOB is ⎢ ⎥ ⎢T¨ ⎥ = ⎢ dis⎥ , = , = ω ··················· derived with a low-pass filter. The DOB in Fig. 2 is derived x ⎢ ⎥ u Im y m (21) ⎢T˙ ⎥ with a first order low-pass filter, and the velocity information ⎣⎢ dis⎦⎥ is usually obtained from the derivative of the position signal T ⎡ dis ⎤ ⎡ ⎤ − / / and a low-pass filter. ⎢00 0 1 Jmn⎥ ⎢Ktn Jmn⎥ The estimated disturbance torque Tˆ is obtained from the ⎢ − ζω −ω2 ⎥ ⎢ ⎥ dis ⎢0 2 0 0 0 ⎥ ⎢ 0 ⎥ ω A = ⎢ ⎥ , B = ⎢ ⎥ , motor speed m, and the motor current Im, as given in (15). ⎣⎢01 0 0⎦⎥ ⎣⎢ 0 ⎦⎥ gdis Tˆdis = (KtnIm + gdisJmnωm) − gdisJmnωm 00 1 0 0 s + g dis C = 1000···························· (22) ··················· (15) ω where g is the cut-off frequency of the low-pass filter used Since the state variable m is measurable, from the state dis matrices A and B, the HDOB is defined using the minimum- in the DOB to attenuate the high-frequency noise. gdis deter- (30) mines the bandwidth of force sensing by the DOB. order observer principles and the Ackermann method .The The compensation current is computed from the estimated state equations of the HDOB are obtained as described in (23) disturbance torque as follows. and (24).⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎢η1⎥ ⎢a0 a1 a2⎥ ⎢η1⎥ ⎢b1⎥ ⎢c1⎥ dis 1 d ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ I = Tˆdis ··································(16) ⎢η ⎥ = ⎢ ⎥ ⎢η ⎥ + ⎢ ⎥ ω + ⎢ ⎥ K ⎣⎢ 2⎦⎥ ⎣⎢ 10a3⎦⎥ ⎣⎢ 2⎦⎥ ⎣⎢b2⎦⎥ m ⎣⎢c2⎦⎥ Im tn dt η η By feeding back the compensation current, robust acceler- 3 01a4 3 b3 c3 ation control is achieved. ··················· (23)

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is a common technique used for dynamic torque estimation and compensation to achieve robustness in robot systems, the high robustness of the robot manipulator is diﬃcult to realize since the DOB exhibits estimation delay due to pole alloca- tion. Therefore, we have proposed a load torque estimation method using the instantaneous state observer (ISOB) for industrial robots. The ISOB overcomes the estimation delay presented by the DOB, and attains instantaneous load torque estimation of a robot arm (21) (22). The load torque compensa- Fig. 3. Plant system of the high-order disturbance observer tion based on the instantaneous load torque estimation makes the robot manipulator highly robust. The ISOB is designed for the robot manipulator modeled as a two-inertia system with an integration of the accelerometer to measure the load-side acceleration. The state equation of the two-inertia system including the load torque τL is expressed as follows. ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎢ω ⎥ ⎢−D /J 0 −K /R J ⎥ ⎢ω ⎥ d ⎢ M⎥ ⎢ Mn Mn Sn gn Mn⎥ ⎢ M⎥ ⎢ω ⎥ = ⎢ − / / ⎥ ⎢ω ⎥ ⎣⎢ L ⎦⎥ ⎣⎢ 0 DLn JLn KSn JLn ⎦⎥ ⎣⎢ L ⎦⎥ dt θS 1/Rgn −10θS ⎡ ⎤ ⎡ ⎤ / ⎢KTn JMn⎥ ⎢ 0 ⎥ + ⎢ ⎥ − ⎢ / ⎥ τ ······ ⎣⎢ 0 ⎦⎥ Icmd ⎣⎢1 JLn⎦⎥ L (31) 0 0 ω ω θ Fig. 4. Structure of the high-order disturbance observer where M is motor velocity, L is load-side velocity, and S is torsional angle. JM and JL denotes motor side and load side ⎡ ⎤ inertia, respectively. DM and DL denotes motor side and load ⎢ ¨ ⎥ ⎡ ⎤ ⎡ ⎤ ⎢Tdis⎥ ⎢η ⎥ ⎢k ⎥ side viscosity coeﬃcients, respectively. K is torsion spring ⎢ ⎥ ⎢ 1⎥ ⎢ 1⎥ S ⎢ ˙ ⎥ = ⎢η ⎥ + ⎢k ⎥ ω ························· constant, and R is gear ratio. I is the current command. ⎢Tdis⎥ ⎣⎢ 2⎦⎥ ⎣⎢ 2⎦⎥ m (24) g cmd ⎣ ⎦ η T 3 k3 Subscript n denotes the nominal value. dis From (31), the derivative of the load-side velocity can be where obtained directly usingω ˙ L = aL, when the load-side acceleration is detected using an acceleration sensor. Using aL, 2 k1 k2 k3 a0 =−2ζω0, a1 =−ω , a2 = , a3 = , a4 = the load torque τ is estimated as expressed in the following 0 J J J L mn mn mn equation. ··················· (25) D K = − ζω − ω2 + k1k3 ·················· = ω = − Ln ω + Snθ − 1 τ b1 2 0k1 0k2 (26) aL ˙ L L S L Jmn JLn JLn JLn k2k3 ⇔ τL = KSnθS − DLnωL − JLnaL ··············· (32) b2 = k1 + ································ (27) Jmn Equation (32) shows that the load torque τL is obtained in- k2 3 stantaneously using the load-side acceleration aL.Tocom- b3 = k2 + ································· (28) Jmn pensate for the initial state error, the state observer is de- ω Ktnk1 Ktnk2 Ktnk3 signed using the observable output M. The observer gain c1 = − , c2 = − , c3 = − ······ (29) T Jmn Jmn Jmn k = k1 k2 k3 is designed to express the state observer of the two-inertia system. The state equation of the ISOB is where k1, k2 and k3 are the elements of the observer gain presented as follows. matrix Ke which is derived by the Ackermann formula and ⎡ ⎤ is determined by the observer pole. The disturbance torque D K ⎡ ⎤ ⎢− Mn − − Sn ⎥ ⎡ ⎤ estimated by the HDOB is obtained using the estimated val- ⎢ k1 0 ⎥ ⎢ ωˆ M ⎥ ⎢ J R J ⎥ ⎢ωˆ M⎥ ˆ ˆ d ⎢ ⎥ ⎢ Mn gn Mn ⎥ ⎢ ⎥ ues T¨ , T˙ and Tˆ as given in (30). The structure of the ⎢ ωˆ ⎥ = ⎢ −k 00⎥ ⎢ωˆ ⎥ dis dis dis ⎣⎢ L ⎦⎥ ⎢ 2 ⎥ ⎣⎢ L ⎦⎥ HDOB is illustrated in Fig. 4. dt θˆ ⎢ 1 ⎥ θˆ S ⎣⎢ − k −10⎦⎥ S R 3 ¨ˆ + ζω ˙ˆ + ω2 ˆ gn Tdis 2 0Tdis 0Tdis ⎡ ⎤ Tˆ = ················ ⎢ KTn ⎥ ⎡ ⎤ ⎡ ⎤ dis 2 (30) ⎢ ⎥ ω ⎢ ⎥ ⎢0⎥ ⎢k1⎥ 0 ⎢ JMn ⎥ ⎢ ⎥ ⎢ ⎥ + ⎢ ⎥ + ⎢ ⎥ + ⎢ ⎥ ω ········· 3.3 Instantaneous State Observer for Load Torque ⎢ ⎥ Icmd ⎣⎢1⎦⎥ aL ⎣⎢k2⎦⎥ M (33) (21) (22) ⎢ 0 ⎥ Estimation In recent years, there has been a growing ⎣ ⎦ 0 k3 0 interest in robot motion control. Rapid and precise robot mo- ⎡ ⎤ ⎢ω ⎥ tion control is vital to improve productivity and quality. Ro- ⎢ ˆ M⎥ τ = − ⎢ω ⎥ − ··········· bustness plays a central role in the design of a robot control ˆL 0 DLn KSn ⎣⎢ ˆ L ⎦⎥ JLnaL (34) scheme to achieve the above objective. Although the DOB θˆS

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susceptible to the effect of derivative noise. Hence, the force-sensing performance of the DOB is degraded by noise. Noise also limits the bandwidth of force sensing by the DOB. Therefore, to address the noise problem, the Kalman filter is utilized for velocity estimation. Using the Kalman filter, we can reduce the noise of the measurement system effectively. As a result, the performance of the control system and the force-sensing bandwidth is improved. Here, velocity estimation by the Kalman filtering is based on the position information measured by an encoder. The state, control input and observation matrices corresponding to (1) and (2) are described in (35). Fig. 5. Block diagram of the instantaneous state observer ⎡ ⎤ ⎡ ⎤ ⎢1 T 0.5T 2⎥ ⎢0⎥ ⎢ s s ⎥ ⎢ ⎥ A = ⎢01 T ⎥ , B = ⎢0⎥ , H = 100····· (35) Equations (33) and (34) show that the ISOB consists of a ⎣ s ⎦ ⎣ ⎦ 00 1 0 state observer for a two-inertia system with an acceleration input, and achieves instantaneous load torque estimation us- x(k) is the vector of the estimated state, including the posi- ing the estimated state variablesω ˆ L, θˆS , and the measured tion, velocity, and acceleration; z(k) is the measured position, load-side acceleration aL. The block diagram of the ISOB is which is corrupted by noise. Ts is the sampling time of con- shown in Fig. 5. trol system. The estimation process by the Kalman filter is In our research, the acceleration sensor, which is used with then carried out as given in (5)–(9) in Section 2. the ISOB, is a DC-response accelerometer (PCB Piezotron- 4.1.2 Force Control of a Linear Shaft Motor Sys- ics: 3711B) with bandwidth of 1 kHz. The load torque esti- tem Using Position-sensor-based Kalman Filter (PKF) mation delay by the ISOB is not dependent on the observer and Disturbance Observer (14) Figure 6 shows the block pole. However, the estimation delay is determined by the diagram of the force control using position-sensor-based bandwidth of acceleration sensor. Kalman filter and disturbance observer. Here, the velocity es- Moreover, the accelerometer has offset and high-frequency timation is performed by PKF using only the encoder output. noises. For eliminating the sensor offset: because we use The DOB estimates the disturbance force using motor cur- an acceleration sensor capable of measuring DC response, rent reference and velocity estimated by PKF. This method is the sensor can detect gravitational acceleration. Therefore, applied for a linear shaft motor. The control algorithm is im- we calibrate the offset caused by acceleration of gravity plemented in FPGA to shorten the sampling time so that the in the measured acceleration easily. For suppressing high- force sensing bandwidth of DOB can be widened by increas- frequency noises: because the bandwidth of acceleration sen- ing the cut-off frequency of the low-pass filter used in the sor is 1 kHz, the noise with frequency higher than 1 kHz is DOB. Figure 7 shows experimental devices of force control suppressed. To suppress the noise with frequency smaller system. than 1 kHz in acceleration signal, we apply the VNC Kalman The effectiveness of this method is evaluated through ex- filter to estimate the load acceleration. The VNC Kalman fil- periments of force control using DOB with conventional ter reduces the vibration of impulsive responses and suppress velocity estimation and force control using DOB with PKF. the high-frequency noises in acceleration signal. Figure 22 in In the experiments, the force sensing bandwidth of DOB is Section 4.3 shows the effect of noise suppression using VNC set to 6280 rad/s, the cut-off frequency of conventional ve- Kalman filter. locity estimation is also set to 6280 rad/s, and the force command is a constant of 0.5 N. The motor movements are stim- 4. Applications of Motion Control based on ulated by human force applied on the motor. The impact Kalman Filter and Disturbance Observer motions are caused by the collision between the motor and This section introduces the methods to integrate the a rubber piece. Figure 8 shows the experimental results of Kalman filter and the DOB in specific applications of mo- force responses. The force response estimated by the DOB tion control and provides evaluations on the performance of with conventional velocity estimation is totally corrupted by the control schemes. noise at the bandwidth of 6280 rad/s as shown in Fig. 8(a). 4.1 Motion Control Using Position-sensor-based Whereas in Fig. 8(b), the DOB using PKF attains the smooth Kalman Filter and Disturbance Observer (14)–(16) force responses with very low noise level. These results in- 4.1.1 Position-sensor-based Kalman Filter for Veloc- dicate that the DOB using PKF provides the improved force ity Estimation The disturbance observers described in sensing performance in terms of noise reduction and widen- Sections 3.1 and 3.2 use the information of the current refer- ing force sensing bandwidth. ence and motor velocity as inputs to estimate the disturbance 4.1.3 Force Control of a Ball-screw System Us- force. Hence, the performance of force estimation by the ing Friction-free High-order Disturbance Observer and DOB is related to how the velocity is accurately estimated. Position-sensor-based Kalman Filter (15) Figure 9 shows Conventionally, the motor velocity is estimated based on the the force control of a ball-screw system using friction-free derivative of the position signal with respect to time. The HDOB and PKF. Here, the velocity estimation is also per- position information is simply obtained from an encoder. formed by PKF using the encoder output only. The HDOB However, the conventional velocity estimation method is uses motor current reference and velocity estimated by PKF

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Fig. 6. Block diagram of force control using PKF and DOB (a) Block diagram of the force control system

(b) Experimental device: Servo motor with a ball-screw Fig. 9. Force control based on HDOB and PKF

Fig. 7. Experimental devices for force control of a linear shaft motor

Fig. 10. Torque response estimated by conventional DOB distorted by friction

The design of dither signal is as follows (15). In our paper, we selected ζ = 0 so that the dither signal is an undamped oscillation, because we desire that the dither signal always has effect during control system operation. The amplitude of dither signal is determined experimentally so that it does not cause vibration in the control system, and is based on (a) Estimated by DOB using conventional velocity estimation the peak-to-peak amplitude of torque signal deteriorated by friction (ΔT). Here, the peak-to-peak amplitude of torque signal is 0.00545 Nm as shown in Fig. 10. Hence, the corresponding current value is ΔI =ΔT/Ktn = 0.011 A, with Ktn = 0.498 Nm/A. The amplitude of dither signal is selected with a value higher than ΔI so that the effect of friction can be reduced provided that it does not generate system vibration. In this paper, the dither is added to the current reference and has the amplitude of 0.015 A. For determination of dither frequency, we conducted the experiments of force control using HDOB with different fre- (b) Estimated by DOB using PKF quencies of dither signal, under conditions of constant torque Fig. 8. Force responses at force sensing bandwidth of 6280 rad/s command of 0.1 Nm and constant velocity of 10.5 rad/s. Here, the HDOB with dither signal performs the force es- to estimate the disturbance force with the elimination of os- timation with the observer pole of 300 rad/s. We investigate cillatory component caused by dither. This method is applied the relationship between the dither frequency and the peak- for a ball-screw system exhibiting high friction. The friction to-peak amplitude of torque signal estimated by HDOB. The compensation is based on dithering method with dither fre- experimental results are shown in Fig. 11. The results show quency of 5 Hz. The control algorithm is also implemented that the peak-to-peak amplitude of torque signal significantly in FPGA to shorten the sampling time and make it possible to decreases at dither frequency of 5 Hz, then continues to a widen the force sensing bandwidth of HDOB by increasing little decrease and reaches the saturation state at dither fre- observer pole. quencies 10 Hz, 15 Hz, and 20 Hz. These results indicate that

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Fig. 13. Torque responses estimated by conventional Fig. 11. Experimental results of relationship between DOB using Kalman ﬁlter (without dither signal) and dither frequency and peak-to-peak amplitude of torque HDOB using Kalman ﬁlter (with dither signal) at force / estimated by HDOB sensing bandwidth of 1000 rad s

with the dither signal, the effect of friction in the estimated torque is reduced, and the oscillatory component caused by dither is also eliminated. These results confirm the effectiveness of dither signal inputting. 4.2 Motion Control Using Multi-sensor-based Kalman Filter and Disturbance Observer (14) (17)–(20) 4.2.1 Multi-sensor-based Kalman Filter (MKF) for Velocity Estimation Although the position-sensor-based Kalman filter can reduce noise efficiently and the DOB can obtain improved force estimation, the limitation of this method is that the accuracy and bandwidth of the measuring system are subject to the performance of the encoder. For Fig. 12. Torque responses estimated by HDOB using the condition of only position measurement, imprecise ve- Kalman filter and HDOB using conventional velocity estimation with observer pole at 1000 rad/s locity estimation often occurs during impact motions with high accelerations due to bandwidth limitation of the measurement device. Therefore, a multi-sensor fusion in Kalman friction in force estimation is effectively reduced by select- filter is employed to obtain the accurate velocity estimation, ing dither signal with frequency of 5 Hz or of higher value. especially in impulsive responses. Therefore, we determine to use the 5 Hz-dither signal with In our system, we use the linear encoder Renishaw HDOB for the force control of ball-screw system. RGH24Y with the maximum speed of 0.25 m/sandresolu- To verify the effectiveness of this method, the force con- tion of 0.1 μm. The control sampling time is 5 μs, and we trol experiment of a ball-screw system was carried out to per- obtain position information in every control sampling pe- form the torque tracking with a constant torque command of riod. Hence, the bandwidth of the position measurement is 0.1 Nm. The observer pole is set to 1000 rad/s. Figure 12 100 kHz. The performance of velocity estimations by PKF presents torque responses estimated by HDOB using conven- and MKF are dependent on the measurement devices and the tional velocity estimation and HDOB using PKF (both with bandwidth of Kalman filter, which is dependent on the noise additional dither signal). The signal estimated by the HDOB characteristics (R and Q matrices). Figures 14 and 15 show without Kalman filter is highly corrupted by very high noise the simulation results of the bandwidth limitation of estima- level when observer pole is set to 1000 rad/s. However, the tions using PKF and MKF. signal estimated by the HDOB using PKF is significantly As shown in Fig. 14, the PKF has the position-estimation improved because the noise in the estimated torque is sup- bandwidth of 1130 rad/s. Since the PKF uses only position pressed effectively and the oscillatory disturbance caused by sensor as the input to estimate velocity, the bandwidth of dither is eliminated. The results confirm that the force control position estimation determines the bandwidth of velocity es- using the HDOB and the Kalman filter with a dither sig- timation by the PKF. However, for the MKF, the position- nal achieves high-performance force sensing with a widened estimation bandwidth is 940 rad/s while the acceleration es- bandwidth of 1000 rad/s based on friction-free and noise-free timation has a wider bandwidth of 7800 rad/sasshownin force observation. Fig. 15. Since the MKF uses both position sensor and accel- Moreover, Fig. 13 presents the comparison of the torque es- eration sensor as the inputs to estimate velocity, these band- timations between the conventional DOB using Kalman filter widths determine the bandwidth of velocity estimation by the (without dither signal) and the HDOB using Kalman filter MKF. The wider bandwidth of acceleration estimation con- (with dither signal) at force sensing bandwidth of 1000 rad/s. tributes to the velocity estimation in impact motion with high The estimated torque without dither has unsmooth response frequency accelerations. Therefore, the velocity estimation in caused by friction. For the HDOB with the Kalman filter and impact motions by the MKF is more accurate than the PKF

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covariance matrices Q and R for MKF is described as follows. ⎡ ⎤ ⎢Q11 00⎥ = T = ⎢ ⎥ ············· Q E ww ⎣⎢ 0 Q22 0 ⎦⎥ (37) 00Q33 R 0 R = E vvT = 11 ····················· (38) 0 R22 where Q and R are nonnegative deﬁnite matrices and E[]denotes the expected value. Elements of Q and R are deﬁned as follows.

R11 = Var [Δxm] ······························· (39)

Fig. 14. Bandwidth of position estimation by PKF R22 = Var [Δx¨m] ······························· (40)

Q11 = Var [Δxe] ······························· (41)

Q22 = Var [Δx˙e] ······························· (42)

Q33 = Var [Δx¨e] ······························· (43)

where Var [ ] denotes variance, Δxm is the difference between the measured position and the desired position, Δx¨m is the difference between the measured acceleration and the desired acceleration. Δxe, Δx˙e,andΔxë are the differences between the predicted values of position, velocity, and acceleration, and the desired values of position, velocity and acceleration, respectively. Δxm is calculated from the sets of position data measured by an encoder. Δx¨m is calculated from the sets of Δ (a) Bandwidth of position estimation acceleration data measured by an acceleration sensor. xe, Δx˙e and Δxë are calculated from the sets of filter’s predicted values of position, velocity, and acceleration, given the corresponding sets of sample data of position measured by encoder and data of acceleration measured by acceleration sensor. Variances of Δxm, Δx¨m, Δxe, Δx˙e and Δxë are computed as follows. |X − μ|2 + ... + |X − μ|2 Var [X] = 1 n ············· (44) n where X + X + ...+ X μ = 1 2 n ·························(45) n X stands for Δxm, Δx¨m, Δxe, Δx˙e or Δxë,andn is the data (b) Bandwidth of acceleration estimation number of the sample data set. The value of matrix R can be Fig. 15. Bandwidth of estimations by MKF easily determined based on the available measured position data, and equations (39), (40), (44) and (45). The values of matrix Q are selected by trial and error. The trial and error because the velocity estimation performance by the PKF is process is carried out by fixing R value, running Kalman filter limit by position estimation bandwidth. With the improved offline with the data set of measured position, obtaining the velocity estimation by MKF, it is possible for the DOB to data sets of predicted values, calculating Q values based on achieve high force sensing performance. (41), (42), (43), (44) and (45), and running filter again. The In the multi-sensor measuring system, the measurement selected values of Q are those enabling the estimated state of process model is also given by (1) and (2). x is still the state (k) filter to achieve the proper values. vector of position, velocity and acceleration. z is the vec- (k) Figure 16 shows the experimental results to evaluate the tor of measured variables comprising the position measured velocity estimation of the Kalman filter using multi-sensor by encoder and the acceleration measured by accelerometer. and that of the Kalman filter using only encoder. The exper- Hence, the state, control input and observation matrices are imental system is shown in Fig. 7. These results show that described as follows. ⎡ ⎤ ⎡ ⎤ there is good agreement between the velocity responses es- ⎢1 T 0.5T 2⎥ ⎢0⎥ ⎢ s s ⎥ ⎢ ⎥ 100 timated by both methods except for the impact motion re- A = ⎢01 T ⎥ , B = ⎢0⎥ , H = ⎣ s ⎦ ⎣ ⎦ 001 sponse. During the impact motion with high acceleration, 00 1 0 the velocity response estimated by MKF is much improved ··················· (36) as shown in Fig. 16(b). These results confirm the advantage The estimation process by Kalman filter is then performed of acceleration measurement in obtaining accurate velocity as described in (5)–(9) in Section 2. The design of the estimation by Kalman filter.

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(a) Velocity responses

Fig. 18. Block diagram of force control using Multi- sensor-based Kalman ﬁlter and disturbance observer

(b) Magniﬁcation of impact motion in (a) Fig. 16. Experimental results of velocity estimation

(a) Estimated by Position-sensor-based Kalman ﬁlter and DOB

Fig. 17. Acceleration response of impact motion measured by acceleration sensor

To confirm the accuracy of the velocity estimation by MKF in Fig. 16, we investigate the impact acceleration response (b) Estimated by Multi-sensor-based Kalman filter and DOB measured by acceleration sensor as shown in Fig. 17. The Fig. 19. Force responses at force sensing bandwidth of 6280 rad/s impact acceleration response has the high frequency oscillations that are reflected in the velocity estimation by the MKF in Fig. 16. In contrast, the velocity estimation by PKF has no with PKF and force control using DOB with MKF. In the such high frequency oscillations during impact motion. It is experiments, the force sensing bandwidth of DOB is set to the acceleration information that contributes to the estimation 6280 rad/s, the cut-off frequency of conventional velocity es- of velocity during impact motion using the MKF. timation is also set to 6280 rad/s, and the force command is 4.2.2 Force Control of a Linear Shaft Motor System a constant of 0.5 N. The motor movements are stimulated by Using Multi-sensor-based Kalman Filter (MKF) and Dis- human force applied to the motor. The impact motions are turbance Observer (14) Figure 18 shows the block dia- caused by collision between the motor and a rubber piece. gram of the force control using multi-sensor-based Kalman Figure 19 displays the force responses at the force sensing filter and disturbance observer. Here, the velocity estima- bandwidth of 6280 rad/s. The results show that both the DOB tion is performed by MKF using multi-sensor information of using MKF and the DOB using PKF obtain the smooth force encoder and acceleration sensor. The DOB estimates the dis- responses with very low peak-to-peak noise levels. From turbance force using motor current reference and velocity es- these results, it is clear that both methods provide the good timated by MKF. force sensing performance. However, since the DOB with This method is applied for a linear shaft motor. The control MKF achieves accurate velocity estimation during the im- algorithm is implemented in FPGA to shorten the sampling pact motion by the Kalman filter with multi-sensor, the im- time such that the force sensing bandwidth of DOB can be pact force response of this method is improved significantly widened by increasing the cut-off frequency of the low-pass compared to that of the DOB using Kalman filter with only filter used in the DOB. The effectiveness of this approach is encoder. These results confirm that the combination of multi- evaluated through experiments of force control using DOB sensor-based Kalman filter and the DOB attains the high

9 IEEJ Journal IA, Vol.7, No.1, 2018 Disturbance Observer and Kalman Filter Based Motion Control（Thao Tran Phuong et al.） performance force sensing based on the improved velocity information. 4.3 Motion Control of Industrial Robot Using Vari- able Noise-Covariance Kalman Filter Based Instanta- neous State Observer (21)–(23) As presented in Section 3.3, the load torque compensation based on instantaneous load torque estimation makes the robot manipulator highly robust. The ISOB is designed for a two-inertia system with the integration of an acceleration sensor to measure the load-side acceleration. The performance of load-side torque estimation using the ISOB is related to the measurement of load-side acceleration. Since the ISOB employs an acceleration sensor to directly obtain the load-side acceleration information, the measured acceleration signal is susceptible to the effect of Fig. 20. Block diagram of the VNC-Kalman-filter- noise. Hence, the torque estimation performance of the ISOB based ISOB is also affected by the noise problem. To address the noise problem, a Kalman filter is utilized for load-side acceleration estimation. Normally, the Kalman filter operates under the assumption that the noise characteristics of the measuring process remain constant at every time step. However, in practice, the noise characteristics are uncertain. Especially, in our research, the measurement noise characteristics the of load-side acceleration significantly change during impulsive acceleration responses. Therefore, to suppress the noise in acceleration signal effectively, we proposed an acceleration estimation Fig. 21. Experimental system of industrial robot arm based on a variable noise-covariance (VNC) Kalman filter. Consequently, the integration of the VNC Kalman filter with the ISOB enhances the performance of ISOB. The VNC time of control system and is selected by testing simulations Kalman filter estimates the state of the measuring process for- of actual acceleration signal such that the characteristics of mulated in equations (1) and (2). Here, x(k) is the estimated high frequency and large variation of the noise at impulsive load-side acceleration; z(k) is the measured acceleration infor- acceleration responses are captured by (49). The estimation mation, which is corrupted by noise. The state, control input of the variable noise covariance R is used to run the Kalman and observation matrices are now given as follows. filter for load-side acceleration estimation. The estimation A = 1, B = 0, H = 1·························(46) process by Kalman filter is then performed as described in The covariance matrix of process noise, Q, and the covari- (5)–(9) in Section 2. The updated acceleration estimation by ance matrix of measurement noise, R, are defined as indicated the VNC Kalman filter is applied to the ISOB for load torque in equations (3) and (4). For a steady-state Kalman filter in estimation. The block diagram of the VNC-Kalman-filter- which the noise characteristics are constant, Q and R can be based ISOB is shown in Fig. 20. ff determined using the testing simulations of the measurement In order to verify the e ectiveness of the VNC-Kalman- sensor signal, which is based on the actual experimental data. filter-based ISOB, the numerical simulation results based on In our research, the noise characteristics of the accelera- the speed-control experimental data of a robot arm are pre- tion measurement are considered to be uncertain. Hence, sented. The speed-control experiment was carried out using a variable-noise-covariance estimation is executed to deter- the upper arm of the industrial robot. Figure 21 shows the mine the value of R during the operation of Kalman filter. overall view of the experimental equipment. The robot arm / The estimation of noise covariance R is performed as follows. was operated at the constant speed of 30 rad s so that the end- effector payload fell at a downward posture. The falling of the end-effector payload resulted in the impulsive motion of R = σ when (σ>R ) ······················ (47) St the robot arm and the impulsive acceleration response. Ex- R = R when (0 <σ≤ R ) ················ (48) St St perimental data sets of Icmd, ωM,andaL were obtained dur- where ing the speed-control operation. These experimental data sets N N are used to perform the numerical simulation of the VNC- 1 1 σ = (a − M)2, M = a ········· (49) Kalman-filter-based ISOB. The simulations were conducted N i N i i=1 i=1 with observer pole of 200 rad/s. RSt is the measurement noise covariance corresponding to Figure 22 shows the accelerations estimated by the VNC- the steady-state Kalman filter. N is the length of the measured Kalman filter compared with the actual acceleration signal acceleration data set used to determine R at every sampling measured by the acceleration sensor. These results show that th time. ai is the i element of the N-element-acceleration data VNC-Kalman filter reduces noise in the acceleration estima- set. During the filter operation, the acceleration data set is up- tion, and significantly reduces vibration in the impulsive re- dated every sampling cycle. N is dependent on the sampling sponse. During the impulsive response, the noise covariance

10 IEEJ Journal IA, Vol.7, No.1, 2018 Disturbance Observer and Kalman Filter Based Motion Control（Thao Tran Phuong et al.）

(a) Load-side acceleration Fig. 23. Estimation of variable noise covariance R

(b) Magniﬁcation of impulsive response (A area) Fig. 24. Impulsive load-acceleration responses esti- matedbyKalmanﬁlter

(c) Magnification of steady state (B area) Fig. 22. Load-acceleration responses Fig. 25. Load torque estimated by VNC-Kalman-filter- R of the VNC Kalman filter changes and has high values as based ISOB shown in Fig. 23. Because the bandwidth of estimation by Kalman filter is dependent on R and Q, the changing of R 4.4 Force Control Using Kalman Filter Based Force make the estimation bandwidth change. Hence, the VNC Sensing with Periodic Component Extraction (24) In Kalman filter reduces the vibration of impulsive response Section 3.2, we introduced the HDOB with dither signal to small amplitude effectively, and still reflects the impul- for force sensing operation and elimination of periodic dis- sive response in the acceleration estimation. However, the turbance in force estimation. However, the design of the amplitude of impulsive response is reduced to a certain ex- HDOB is motivated by a state-space approach and the force tent due to estimation bandwidth limitation during impulsive estimation is noisy when the observer pole is increased. response. For the Kalman-filter-based acceleration estima- Besides, in Section 4.1.3, the force control of a ball-screw tion, the noise covariance R is constant during impulsive re- system uses the HDOB with velocity estimation based on the sponse. The results in Fig. 24 show that the Kalman-filter- Kalman filter. Since motor speed is the input of the HDOB, based acceleration estimation cannot suppress the vibration using Kalman filter effectively reduces noise in velocity esti- effectively. mation, and reduces noise in force estimation by HDOB to a The load torque estimated by the VNC-Kalman-filter- certain extent. based ISOB is presented in Fig. 25. This result proves that However, since the design of the HDOB itself does not ff ˙ the estimated impulsive load torque has no e ect of vibration consider the noise suppression of the estimations of Tdis, T dis due to superior acceleration estimation by the VNC-Kalman and T¨ dis, the force estimation by the HDOB becomes noisy filter. The above results confirm that this method achieves when the observer pole is increased. Therefore, even when high performance load torque estimation, and is feasible to the Kalman filter is employed for velocity estimation, this realize the high-performance load acceleration control of in- method has less potential to further widen the force sensing dustrial robots. bandwidth.

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Fig. 27. Torque estimations at DOB pole of 1000 rad/s (5 Hz dither)

Fig. 26. Force control using Kalman-ﬁlter-based force sensing with periodic component extraction

˙ To address the noise problem, the estimations of Tdis, T dis and T¨ dis are performed by Kalman filter. Figure 26 shows the block diagram of force control using Kalman-filter-based force sensing with periodic component extraction. In this control scheme, a dither signal is inserted to the system to reduce friction effect of the ball-screw system. The DOB Fig. 28. FFT of torque responses in Fig. 27 with conventional velocity estimation is used to estimate the oscillatory disturbance force caused by dither. The force tions are usually based on fixed point numbers for reduction estimation by Kalman filter is based on the oscillatory force of FPGA resources and lower power consumption. However, estimated by DOB. the calculations based on fixed point have high quantization Here, we do not combine DOB and Kalman filter for high- noise. On the other hand, the calculations in RT-Linux sys- order torque estimation, because using the Kalman filter as a tem are based on floating point numbers that have much less state observer, we cannot determine the bandwidth of force quantization noise than fixed point calculations. Hence, we sensing beforehand. Therefore, here, we apply the force es- implement the control algorithm with the high-order estima- timation by DOB and the force estimation by Kalman filter tions of Kalman filter in RT-Linux to reduce the effect of cal- serially. First, we determine the force sensing bandwidth of culation noise. DOB to estimate the oscillatory torque. Then, the oscilla- The state, control input and observation matrices for tory torque will be used as an input to Kalman filter to es- Kalman filtering process are given as follows. timate high-order oscillatory torques with noise suppression. ⎡ ⎤ ⎡ ⎤ ⎢1 Ts 0 ⎥ ⎢0⎥ Moreover, we do not use Kalman filter for velocity estima- ⎢ ⎥ ⎢ ⎥ A = ⎢01 T ⎥ , B = ⎢0⎥ , H = 100. tion because of the following reasons: Firstly, the velocity ⎣ s ⎦ ⎣ ⎦ 0 −ω2T 1 − 2ζω T 0 estimation by Kalman filter only reduces the noise in the ve- 0 s 0 s ··················· locity estimation. If it is used as the input of the DOB, the (50) ˙ noise in the force estimation by DOB is reduced to a cer- The estimated states by Kalman filter includeTdis, T dis and tain extent when the DOB pole is increased. However, here, T¨ dis. z(k) is the periodical torque estimated by DOB, which we would like to estimate the high-order torque responses is corrupted by noise. The estimated disturbance torque with- of the oscillatory torque estimated by DOB. The estimations out oscillation component based on Kalman filter is applied of high-order torque are highly affected by noise when the to compute the compensation current to cancel the effects of DOB pole is increased. Therefore, using velocity estimation the disturbance torque on the motor and the modeling errors. by Kalman filter will not have much effect on suppressing In this method, the effect of noise in the force estimation is noise in high-order torque estimation. The high-order torque suppressed effectively since the force sensation is based on estimation with effective noise suppression is performed by estimations generated by the Kalman filter. Accordingly, it Kalman filter. Secondly, we reduce the execution time of the is possible to further widen bandwidth of force sensing by controller by not using Kalman filter for velocity estimation. increasing the pole of DOB. In this method, we use RT-Linux instead of FPGA to im- In order to evaluate the performance and verify the plement the control algorithm with sampling time of 100 μs effectiveness of this method, the numerical simulation results because it is very difficult to implement the whole control al- based on the force-control experimental data of a ball-screw gorithm with the high-order estimations of Kalman filter in system are presented. The force-control experiment was car- FPGA. It is impossible for us to implement this algorithm ried out with a constant force command of 0.1 Nm. A 5 Hz- in FPGA. Moreover, for FPGA implementation, the calcula- dither signal was inserted to the control reference signal. The

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13 IEEJ Journal IA, Vol.7, No.1, 2018 Disturbance Observer and Kalman Filter Based Motion Control（Thao Tran Phuong et al.）

Journal of Basic Engineering, Transactions of the ASME, Vol.82, Series D, Yuki Yokokura (Member) received the B.E. and M.E. degrees in elec- pp.35–45 (1960) trical engineering from Nagaoka University of Tech- (28) S. Katsura, K. Ohnishi, and K. Ohishi: “Transmission of force sensation by nology, Niigata, Japan, in 2007 and 2009, respec- environment quarrier based on multilateral control”, IEEE Trans. on Indus- tively. In 2011, he received Ph.D. degree in integrated trial Electronics, Vol.54, No.2, pp.898–906 (2007) design engineering from Keio University, Yokohama, (29) S. Katsura and K. Ohishi: “Acquisition and analysis of finger motions by skill Japan. From 2010 to 2011, he was a JSPS (Japan So- IEEE Trans. on Industrial Electronics preservation system”, , Vol.54, No.6, ciety for the Promotion of Science) Research Fellow pp.3353–3361 (2007) (DC2 and PD). He was a Visiting Fellow at Keio Uni- (30) K. Ogata: “Modern control engineering”, 3rd Edition, Prentice Hall (1999) versity, and a Postdoctoral Fellow at Nagaoka Uni- versity of Technology in 2011. Since 2012, he has been an Assistant Professor with Nagaoka University of Technology. His research interests include motion control, motor drive, power electronics, and Thao Tran Phuong (Member) received B.S. degree in mechatronics real-world haptics. engineering from University of Technical Education, Ho Chi Minh city, Viet Nam in 2007, M.S. degree in electrical, electronics and information engineer- Kouhei Ohnishi (Fellow) received the B.E., M.E. and Ph.D. in electri- ing and Ph.D. degree in energy and environment sci- cal engineering from the University of Tokyo in 1975, ence from Nagaoka University of Technology, Japan 1977 and 1980. Since 1980, he has been with Keio in 2010 and 2013, respectively. At present, she is University. He served as a President of the IEEE In- a Postdoctoral researcher at Nagaoka University of dustrial Electronics Society in 2008 and 2009 as well Technology, Japan. Her interests include motion con- as a President of IEEJ in 2015 and 2016. Since 2016, trol, robotics, embedded systems, especially human he has been also with KISTEC at Kawasaki, Japan. support applications. He received Purple Ribbon Medal Award from His Majesty the Emperor in 2016 and the IEEJ Meritori- ous Contribution Award in 2017. Kiyoshi Ohishi (Fellow) received the B.E., M.E., and Ph.D. degrees in electrical engineering from Keio University, Yoko- hama, Japan, in 1981, 1983, and 1986, respectively. Roberto Oboe (Member) was born in Lonigo, Italy, on October 26, From 1986 to 1993, Prof. Ohishi was an Associate 1963. He received the Laurea degree (cum laude) Professor with Osaka Institute of Technology, Osaka, in electrical engineering and the Ph.D. degree from Japan. Since 1993, he has been with Nagaoka Uni- the University of Padova, Padova, Italy, in 1988 and versity of Technology, Nagaoka, Japan. He became a 1992, respectively. He is presently Associate Pro- Professor in 2003 and a Vice President in 2015. His fessor of Automatic Control at the Department of research interests include motion control, mechatron- Management and Engineering of the University of ics, robotics and power electronics. He is a Fellow Padova, Vicenza, Italy. His research interests are in member of IEEE from 2015. He is a General chair of IEEE IECON2015. the fields of motion control, telerobotics, haptic de- He is a General chair of IEEE AMC2010, AMC2016 and AMC2018. He is vices, rehabilitation robots and applications and con- a Senior AdCom Member of IEEE IES Society from 2016, and he was an trol of MEMS. AdCom Member (elected) of IEEE IES Society for 12 years from 2004. He received the Outstanding Paper Awards at IECON’85 and Best Paper Awards at IECON’02, IECON’04 from the IEEE Industrial Electronics Society. He Asif Sabanovicˇ (Non-member) Emeritus Professor, Sabanci Univer- received twice “IEEJ Distinguished Paper Award” from IEEJ in 2002 and sity, Istanbul and International University, Sarajevo. 2009, respectively. He received B.S. degree in electrical engineering (in 1970), M.S. degree and Dr.Sci. degree from Uni- versity of Sarajevo. Since 1970 till 1991 he has Chowarit Mitsantisuk (Member) received the B.Eng. degree in elec- been with Energoinvest - Institute for Control and trical engineering from Thammasat University, Thai- Computer Sciences, Sarajevo. From 1991till 2011he land, in 2004, and the M.Eng. degree in Electrical, has been with University of Sarajevo, Department of Electronics and Information Engineering and D.Eng. Electrical Engineering. He has been Visiting Re- degree in energy and environment science from the searcher at Institute of Control Science - Moscow Nagaoka University of Technology, Japan, in 2007 (1975–1976). Visiting Professor at California Institute of Technology, and 2010, respectively. He was also a Postdoctoral Pasadena (1984–1985), Visiting Professor at Keio University, Yokohama Fellow of the Japan Society for the Promotion of Sci- (1991–1992), Full Professor at Yamaguchi University, Ube (1992–1993), ence. He joined the Kasetsart University, Thailand Visiting Professor at University of Maribor- Maribor-Slovenia (Acad. Year in 2012 as a lecturer and was promoted to assistant 1986, 1988, 1989), Head of CAD/CAM and Robotics Department at Tubitak professor in 2015. Dr. Chowarit’s research interests lie mainly in robot- - Marmara Research Centre, Istanbul (1993–1995), Head of Engineering De- created artwork, human-robot collaboration system, haptics, tele-operated partment of B.H. Engineering and Consulting (1995–1999), Full professor robot, and rehabilitation robot. Dr. Chowarit is a Member of the IEEE In- at Sabancı University, Istanbul (1999–2015). He is a corresponding member dustrial Electronics Society. He was the recipient of the IEEE-IES Student of ANU BiH. His publications include 5 books, 2 edited books, 442 articles Scholarship Award at the IECON’09, Best Paper Award at the iEECON’17 and conference papers. He received Best paper award, 2tth July award (the and ECTI-CON’17 and Best Presentation Award at IECON’12. His work on state award in Bosnia and Herzegovina). His fields of interest include Con- the human-robot collaboration system won the 2nd place in 2017 Robot Art trol Systems, Motion Control Systems, Robotics, Mechatronics and Power world-wide competition. electronics.

14 IEEJ Journal IA, Vol.7, No.1, 2018