Stable Force Control of Industrial Robot Based on Spring Ratio and Instantaneous State Observer

Transcription

1 IEEJ Journal of Industry Applications Vol.5 No. pp.3 4 DOI:.54/ieejjia.5.3 Paper Stable Force Control of Industrial Robot Based on Spring Ratio and Instantaneous State Observer Takashi Yoshioka Student Member, Akinori Yabuki Student Member Yuki Yokokura Member, Kiyoshi Ohishi Senior Member Toshimasa Miyazaki Member, Thao Tran Phuong Member (Manuscript received May 3, 5, revised Sep. 9, 5) To achieve force control of an industrial robot, this paper proposes a new force control method based on the spring ratio and the instantaneous state observer. To analyze the behavior of an industrial robot in contact with the environment, this paper analyzes a two-inertia system in contact with the environment. On the basis of the resonance ratio considering the environment, this paper shows that the stability of the resonance ratio control depends on the bandwidth of the torsional torque estimation. To achieve stable resonance ratio control, this paper employs the resonance ratio control with the instantaneous state observer. In addition, a force control system using the I-P force controller and the instantaneous state observer is employed. This paper shows that the resonance ratio of the force control system is determined to be the spring ratio S.Theeffectiveness of the proposed method is confirmed by a numerical simulation and experiments using the industrial robot arm. Keywords: industrial robot, two-inertia system, force control, resonance ratio control, instantaneous state observer. Introduction In the industrial field, industrial robots are used in many situations such as welding, painting, and handling operations. Recently, to expand the application of industrial robots, force control is often considered () (5). The force control of industrial robots is expected to be applied to polishing, assembling, and burring operations. To analyze the behavior of an industrial robot in contact with the environment, investigations of both the behavior of the robot actuator and its contact behavior with the environment are required. Generally, the actuator model of an industrial robot is modeled as a two-inertia system (6) (9). As the force control of two-inertia system, a force control method based on resonance ratio control () () has been proposed () (4). However, these papers do not sufficiently discuss the frequency characteristics of the two-inertia system with the environment. In a two-inertia system without the environment, the resonance ratio is expressed by the inertia ratio. In contrast, in a two-inertia system with the environment, a new resonance ratio is expressed using the inertia ratio, gear stiffness, and environmental stiffness. Therefore, for a twoinertia system with the environment, a new analysis method that considers the effect of the environment is required. Considering this problem, this paper analyzes a two-inertia system in contact with the environment. A resonance ratio that considers the environment is expressed when the twoinertia system contacts the environment. Based on the resonance ratio that considers the environment, this paper shows Nagaoka University of Technology 63-, Kamitomioka-machi, Nagaoka-shi, Niigata 94-88, Japan that the stability of resonance ratio control depends on the bandwidth of a torsional torque observer. To achieve the wide-band estimation of the tortional torque, this paper designs a stable resonance ratio control using an instantaneous state observer (5). In addition, this paper proposes a new force control system based on the resonance ratio control using the instantaneous state observer and I-P force control. This paper shows that the resonance ratio is determined to be a spring ratio S based on the coefficient diagram method (6).Theresonance ratio control included in the force control is designed to satisfy the spring ratio. The spring ratio is the ratio of the environmental stiffness to the total stiffness of the gear and environment. The effectiveness of the proposed method is confirmed by the results of a numerical simulation and experiments using the industrial robot arm.. Stability Analysis of Resonance Ratio Control Considering Environment In this paper, the resonance ratio control is used as the inner loop of the force control system. When the two-inertia system contacts the environment, an analysis that considers the behavior of the environment is required. Figure shows the openloop block diagram of the resonance ratio control used in this paper. Table shows the parameter list used in this paper. For the stability analysis, Fig. is transformed into the equivalent block diagram shown in Fig.. Here, L do (s) expresses the low-pass filter of the disturbance observer. The openloop transfer function G op (s) isexpressedasfollows. G op (s) = a fb M a ref M = KL do (s)p e (s) s P ar (s) + K ( L do (s)) P e (s) () c 6 The Institute of Electrical Engineers of Japan. 3

2 Fig. 3. Bode diagram of openloop transfer function of resonance ratio control (ideal condition) Fig.. Openloop block diagram of resonance ratio control used in this paper Table. Parameter list Load inertia J L.7 [kgm ] Motor inertia J M.8 4 [kgm ] Motor-side viscous friction D M [Nm/(rad/s)] Load-side viscous friction D L 6.9 [Nm/(rad/s)] Gear ratio R g Gear stiffness K S [Nm/rad] Environmental stiffness K e.83 4 [Nm/rad] Fig. 4. Fig. Bode diagram of openloop transfer function of Fig.. Equivalent block diagram of Fig. P ar (s) = s + ζ s + ω ar P e (s) = s + ζ ω e s + ω e K = K S KS + K e Ke ω R ar =, ω e = gj M J L J L ζ = D L J L ( ), ζ = D L J L K e On the condition that the ideal estimation of the torsional torque is achieved, the transfer function of the torsional torque estimation is expressed as L do (s) =. In this case, the openloop transfer function is expressed as follows. G op,ideal (s) = K ( ) s + ζ ω e s + ω e s ( s + ζ s + ω ) () ar For the resonance ratio control, the openloop transfer function is fed back through the feedback gain K f. Thus, the stability of the resonance ratio control is analyzed using the gain margin g m of the openloop transfer function. Figure 3 shows the phase characteristics of Eq. (). These phase characteristics do not exceed 8 [deg] because the relative order number is. In the ideal resonance ratio control, the gain margin g m becomes infinity. Hence, the resonance ratio control is always stable for any value of feedback gain K f. When the torsional torque has an estimation delay, the transfer function of the torsional torque estimation is expressed as L do (s) = g/(s + g). In this case, the openloop transfer function is expressed as follows. gkp e (s) G op,obs (s) = (3) s (s + g) P ar (s) + skp e (s) The phase characteristics of the transfer function of Eq. (3) exceed 8 [deg] because the relative order number is 3. Hence, the gain margin g m becomes a finite value. Figure 4 shows the board diagram of Eq. (3). The board diagram shows that the gain margin decreases by the increment of gear stiffness K S, and it increases by the increment of observer pole g. The maximum value of feedback gain K f for the stable resonance ratio control is obtained from the following 33 IEEJ Journal IA, Vol.5, No., 6

3 Table 3. Numerical data of realizable resonance ratio versus bandwith of observer g = 3 [rad/s] g = [rad/s] g = 68 [rad/s] K Sn K Sn K Sn Fig. 6. Realizable resonance ratio versus observer pole Fig. 5. Enlarged view of Fig. 4 Table. Numerical data of maximum value of feedback gain K f versus bandwith of observer g = 3 [rad/s] g = [rad/s] g = 68 [rad/s] K Sn K Sn K Sn equation and the gain margin g m shown in Fig. 5. K f = gm (4) When the two-inertia system contacts the environment, a new resonance ratio that includes the contact with the environment is determined. In this paper, the new resonance ratio that includes the contact with the environment is expressed as follows. H = ω r (+RS = +R ) 4RS S R K f + R K f R e R K f (5) (ω ) ω r = + KK f + ar + KK f 4KKf ω e, R = J L R gj M, R S = K S, R e = K e The resonance ratio not considering the environment is expressed using inertia ratio R only. In contrast, the new resonance ratio is expressed by inertia ratio R, gear stiffness K S, and environmental stiffness K e. Table 3 and Fig. 6 show that the realizable resonance ratio depends on the observer pole g. The analysis results show that a wide-band observer is required to set a larger resonance ratio on the condition of a high gear stiffness. In addition, this paper conducts the additional analysis (a) Overall view (b) Enlarged view Fig. 7. Bode diagram of openloop transfer function versus environmental stiffness K e for the variation of the environmental stiffness K e. Figure 7 shows the bode diagram of openloop transfer function and Table 4 shows the gain margin of each condition. The gain margin becomes small on the condition that the large environmental stiffness K e is larger than the gear stiffness K S = 9.4e4 [Nm/rad]. The analysis results show that the force control for the hard environment is difficult to achieve. Therefore, the force control becomes difficult on condition that the environmental stiffness is greater than the nominal 34 IEEJ Journal IA, Vol.5, No., 6

4 Table 4. Numerical data of maximum value of feedback gain K f versus environmental stiffness K e g = 3 [rad/s] g = 68 [rad/s] K e =.e3 [Nm/rad] K e = 7.68e3 [Nm/rad] K e =.83e4 [Nm/rad] K e =.e5 [Nm/rad].. Fig. 8. Block diagram of ISOB that considers bandwidth of accelerometer value of the environmental stiffness. 3. Force Control System based on Spring Ratio In this paper, as the outer loop of the resonance ratio control, the I-P force control that has the motor-side velocity feedback is used. An instantaneous state observer (ISOB) (5) is used for the wide-band estimation of the torsional torque τ dism. 3. Instantaneous State Observer considering Bandwidth of Accelerometer The state equation of a twoinertia system, including the load torque τ L, is expressed as follows. d dt ˆω M ˆω L ˆθ S = + D Mn J Mn K Sn R gn J Mn D Ln J Ln K Sn J Ln R gn K Tn J Mn I cmd J Ln ω M ω L θ S τ L (6) Here, the derivative of the load-side velocity is obtained directly using ω L = a L in a case where the load-side acceleration a L is detected using an accelerometer. The following equation is obtained for the estimation of the load torque using a L. a L = ω L = D Ln ω L + K Sn θ S τ L J Ln J Ln J Ln τ L = K Sn θ S D Ln ω L J Ln a L (7) Equation (7) expresses that τ L is obtained instantaneously using a L. To compensate for the initial state error, this paper designs a state observer using an observable output ω M. The observer gain k is used to express the state observer of a two-inertia system. Finally, the state equation of the ISOB is expressed as follows. Equations (8) and (9) express that the ISOB consists of a state observer for a two-inertia system with an acceleration input. ˆx = A o ˆx + b i I cmd + b a a L + kω M (8) ˆτ L = c t ˆx + d t a L (9) ˆx = [ ] T ˆω M ˆω L ˆθ S A o = D Mn J Mn k K Sn R gn J Mn k R gn k 3 b i = [ K Tn J Mn ] T, ba = [ ] T c t = [ ] D Ln K Sn, dt = J Ln The ISOB achieves an instantaneous estimation of the state variable based on Eqs. (8) and (9) under the ideal condition. However, because of the bandwidth of the accelerometer, the estimation bandwidth of the ISOB is restricted. Considering this problem, this paper redesigns the ISOB considering the bandwidth of the accelerometer. In this paper, the transfer function of the accelerometer is modeled as a first-order lowpass filter. The bandwidth of the accelerometer used in this paper is [khz]. a L,meas = g a a L () s + g a Here, based on the bandwidth of the accelerometer, this paper filters the current command input I cmd and the motor-side velocity feedback ω M as follows. I cmd, f ilt = g a I cmd, ω M, f ilt = g a ω M s + g a s + g a As a result, the estimated state variable considering the bandwidth of the accelerometer is expressed as follows. ˆx f ilt = (si A o ) ( ) b i I cmd, f ilt + b a a L,meas + kω M, f ilt = g a (si A o ) (b i I cmd + b a a L + kω M ) s + g a = g a ˆx () s + g a Therefore, considering the bandwidth of the accelerometer, the state estimation has the same phase delay as the accelerometer. 3. Design of Force Control System Figure 9 shows a block diagram of the proposed force control system. The state equation of the proposed force control system is expressed as follows. ẋ = Ax + bτ ref L () τ L = cx (3) x = [ ω M θ S ω L θ L z ] T A = ( ) D M J M + K V K S K f R g J M K e K P K I R g K S J L D L J L K e J L K e b = [ ] T c = [ K e ] Here, this paper ignores the motor-side viscous friction and load-side viscous friction (D M =, D L = ). The characteristic polynomial of the force control system is expressed as follows. 35 IEEJ Journal IA, Vol.5, No., 6

5 Fig. 9. Block diagram of proposed force control system based on resonance ratio control Table 5. Stability index of each standard form CDM [γ, γ, γ 3, γ 4 ] = [.5,,,] Binomial [γ, γ, γ 3, γ 4 ] = [.5,,,.5] D(s) = s 5 + a 4 s 4 + a 3 s 3 + a s + a s + a (4) a 4 = K V a 3 = KK f + ω ar a = ω ark V ( a = ω e KK f + K ) S K P R g a = K S ω R ek I g The feedback gains satisfying the arbitrary stability index (γ = a /(a a ), γ = a /(a 4a 3 ), γ 3 = a 3 /(a 4a ), γ 4 = a 4 /a 3) are expressed as follows. K V = γ 4 γ3 (5) K f = (γ 3 γ 4 ) K (6) K P = R ( g ω γ4 ω ) ar ar (γ K S γ ω 3 γ 4 ) (7) e γ 4 R g ω ar K I = γ γ ω 3 γ3 K S ω ar (8) e The equivalent time constant τ is expressedas follows, and is expressed by the anti-resonance frequency. τ = a = γ γ γ3 (9) a This paper obtains the feedback gains and equivalent time constant based on the stability index of two standard forms shown in Table 5. The feedback gains and equivalent time constant based on the coefficient diagram method (CDM) are expressed as follows. K V,CDM = K f,cdm = 3 K Fig.. system Block diagram of conventional force control K P,CDM = R ( g ω ω ) ar ar 3 K S ω e K I,CDM = 5 R g τ CDM = 5 K S ω ar ω e ω 3 ar The feedback gains and equivalent time constant based on the binomial standard form are expressed as follows. K V,Binom = 5 ωar K f,binom = 4 K K P,Binom = R g K S ω ar K I,Binom = 4 R g ω ar K S ω e ( 5 ω ) ar 4 4 ω e ω 3 ar τ Binom = Determination of Spring Ratio Using the feedback gain K f, the resonance ratio H is determined as follows, and is also expressed by the anti-resonance frequency. This paper determines the following resonance ratio to 36 IEEJ Journal IA, Vol.5, No., 6

6 be anti-resonance ratio H a because anti-resonance ratio H a is expressed as the ratio of anti-resonance frequency to environmental anti-resonance frequency ω e. In addition, H a is transformed into Eq. (). Hence, this paper also determines the calculated resonance ratio to be a spring ratio S. H = H a = S = γ 3 γ 4 + γ 3 γ 4 + (γ3 γ ) ( ) 4 ωe (γ3 γ 4 ) () (γ3 γ ) 4 (γ3 γ 4 ) K e () Using the CDM, the anti-resonance ratio H a and spring ratio S are expressed as follows. H a,cdm = ( ωe ) () S CDM = (3) The spring ratio becomes the largest value S CDM = onthe condition of K e K S, and the spring ratio becomes the smallest value S CDM = 3 on the condition of K e K S. Using the binomial standard form, the anti-resonance ratio H a and spring ratio S are expressed as follows. H a,binom = S Binom = K e ( ωe 5 + ) (4) K e (5) The spring ratio becomes the largest value S Binom = 5on the condition of K e K S, and the spring ratio becomes the smallest value S Binom = on the condition of K e K S. 3.4 Simulation Results Figure shows the numerical simulation results of the force control system. In this numerical simulation, the spring ratio is set to S CDM =.97. Figures (a) and (b) shows an unstable response because the bandwidth of the torsional torque observer is not sufficient in comparison with the designed spring ratio. In contrast, Fig. (c) shows a stable response under the condition of the designed spring ratio because the ISOB achieves a wide-band estimation of the torsional torque. Figure shows the numerical simulation results of each standard form. Table 6 lists the design parameters of the numerical simulation. S Binom becomes different value because γ 3 and γ 4 of the binomial standard form are different from γ 3 and γ 4 of the CDM. The force control system based on the CDM has the similar force response as the force control system based on the binomial standard form. Considering the results of numerical simulation, this paper adopts the force control based on the CDM. Generally, the industrial robot has the load inertia variation by the posture change of the industrial robot. To evaluate the (a) Conventional method (g = 3 [rad/s]) (b) Conventional method (g = [rad/s]) (c) Proposed method (instantaneous state observer) Fig.. Comparison between conventional method and proposed method by numerical simulation Fig.. Table 6. (a) Overall view (b) Enlarged view Numerical simulation results of each standard form Design parameters of numerical simulation Spring ratio S CDM =.97 CDM Stability index [γ, γ, γ 3, γ 4 ] = [.5,,,] Pole assignment p = [, 8±63.3i, 99±3i] Spring ratio S Binom =. Binomial Stability index [γ, γ, γ 3, γ 4 ] = [.5,,,.5] Pole assignment p = [ 79, 79, 79, 79, 79] robustness against the load inertia variation, this paper conducts the numerical simulation. Figure 3 shows the numerical simulation results against the load inertia variation. It is noted that 4J Ln expresses the maximum payload of tested industrial robot. The numerical simulation results show that the proposed method has the robustness against the load inertia variation. 37 IEEJ Journal IA, Vol.5, No., 6

7 Table 7. Configuration of experimental system Sampling time [μs] Resolution of rotary encoder 7 [bit] Bandwidth of accelerometer [khz] Bandwidth of force sensor [khz] Fig. 3. Numerical simulation results against inertia variation Table 8. Design parameters of force control system Poles of instantaneous state observer p ISOB = [ 3, 3, 3] Stability index [γ, γ, γ 3, γ 4 ] = [.5,,,] Environmental stiffness (environment-a) K e,a =.83 4 [Nm/rad] Spring ratio (environment-a) S A =.97 Environmental stiffness (environment-b) K e,b = [Nm/rad] Spring ratio (environment-b) S B =.99 Fig. 6. Experimental results of proposed force control system Fig. 4. Overview of experiments using industrial robot arm (a) Experiments using environment-a (b) Experiments using environment-b Fig. 7. Experimental results of proposed force control system on condition of different environment Fig Experimental Results Block diagram of experimental system 4. Experimental Setup To confirm the effectiveness of the proposed method, this paper conducts experiments using an industrial robot arm. Figure 4 shows an overview of the experiments using the industrial robot arm. Figure 5 shows a block diagram of experimental system. Table 7 shows a configuration of experimental system. A DC-response accelerometer (PCB Piezotronics: 37B) is attached to measure the load-side acceleration. A force sensor (WACOH-TECH Inc.: WEF-6A5--RC4-A) is attached to measure the reaction force of the end-effector. In the experiments, the industrial robot arm pushes the elastic block using the force control. A hard elastic block (environment-a) and soft elastic block (environment-b) are used as the environment. Table 8 lists the design parameters of the force control system. 4. Experimental Results Figure 6 shows the experimental results of the proposed force control system. The experimental condition is same as the numerical simulation. The experimental results indicate a tendency similar to the numerical simulation results. A small vibration in the transient response is caused by the bandwidth of the force sensor. In Appendix, this paper conducts the numerical simulation on condition of the bandwith limitation of force sensor. Figure 7 shows the experimental results of the proposed force control system under the condition that the operating point and environment are changed. The experimental results show that the force response is stable at each operating point. In addition, the experimental results show that the force response is stable response on the condition of each environment. 5. Conclusion To enhance the force control of industrial robot, this paper proposes a new force control method based on the spring ratio and instantaneous state observer. To analyze the behavior of an industrial robot in contact with the environment, this 38 IEEJ Journal IA, Vol.5, No., 6

8 paper analyzes the two-inertia system in contact with the environment. For the stable resonance ratio control, this paper uses the instantaneous state observer. In addition, this paper proposes a new force control system using the I-P force control and the resonance ratio control based on the instantaneous state observer. Moreover, this paper shows that the resonance ratio considering the environment is determined by the spring ratio S. The effectiveness of the proposed method is confirmed by the results of a numerical simulation and experiments using an industrial robot arm. The results show that the proposed method is effective for the stable force control of an industrial robot arm. As the future works, we will apply the force control considering the angular transmission error of reduction gear. Appendix This paper considers the bandwidth of accelerometer, however, this paper assumes the ideal force sensor. Therefore, the bandwidth limitation of force sensor affects the force response. To confirm the effect of the bandwidth of force sensor, the authors conduct the numerical simulation. app. Fig. shows the numerical simulation results under the bandwidth limitation of force sensor. The results show that the small vibration occurs in the transient response because of the bandwidth limitation of force sensor. References ( ) T. Murakami, F. Yu, and K. Ohnishi: Torque Sensorless Control in Multidegree-of-Freedom Manipulator, IEEE Trans. Ind. Electron., Vol.4, No., pp (993) ( ) S. Tungpataratanawong, K. Ohishi, T. Miyazaki, and S. Katsura: Force Sensor-less Workspace Virtual Impedance Control Considering Resonant Vibration for Industrial Robot, IEEJ Trans. IA, Vol.7, No., pp. 8 (7) ( 3 ) Y. Kimura, S. Oh, and Y. Hori: Novel Reaction Force Control Design based on Bi-articular Driving System using Intrinsic Muscle Viscoelasticity, Proceedings of IEEE ICM, pp.85 8 () ( 4 ) T.T. Phuong, K. Ohishi, Y. Yokokura, and C. Mitsantisuk: FPGA-Based High-Performance Force Control System With Friction-Free and Noise-Free Force Observation, IEEE Trans. Ind. Electron., Vol.6, No., pp (4) ( 5 ) W. Maebashi, K. Ito, K. Matsuo, and M. Iwasaki: High-Precision Sensorless Force Control by Mode Switching Controller for Positioning Devices with Contact Operation, IEEJ Trans. IA, Vol.34, No.5, pp (4) (in Japanese) ( 6 ) G. Ferretti, G.A. Magnani, and P. Rocco: Impedance Control for Elastic Joints Industrial Manipulators, IEEE Trans. Robotics and Automation, Vol., No.3, pp (4) ( 7 ) T. Kawakami, K. Ayusawa, H. Kaminaga, and Y. Nakamura: High-fidelity Joint Drive System by Torque Feedback Control using High Precision Linear Encoder, Proceedings of IEEE ICRA, pp () ( 8 ) S. Yamada, K. Inukai, H. Fujimoto, K. Omata, Y. Takeda, and S. Makinouchi: Proposal of Joint Torque Control for a Two-inertia System using a Load-side Encoder, Proceedings of IEEJ Technical Meeting on Mechatronics Control, MEC-4-57, pp.57 6 (4) (in Japanese) ( 9 ) N. Shimada, T. Yoshioka, K. Ohishi, and T. Miyazaki: Quick and Reliable Contact Detection for Sensorless Force Control of Industrial Robots for Human Support, Proceedings of IEEE ISIE, pp () () K. Yuki, T. Murakami, and K. Ohnishi: Vibration Control of a Mass Resonant System by the Resonance Ratio Control, IEEJ Trans. IA, Vol.3, No., pp.6 69 (993) (in Japanese) ( ) Y. Hori: -Inertia System Control using Resonance Ratio Control and Manabe Polynomials, IEEJ Trans. IA, Vol.4, No., pp (994) (in Japanese) ( ) S. Katsura and K. Ohnishi: Force Servoing by Flexible Manipulator Based on Resonance Ratio Control, IEEE Trans. Ind. Electron., Vol.54, No., pp (7) ( 3) C. Mitsantisuk, M. Nandayapa, K. Ohishi, and S. Katsura: Resonance Ratio Control Based on Coefficient Diagram Method for Force Control of Flexible Robot System, Proceedings of IEEE AMC () (4) S. Anayama, K. Ohishi, Y. Yokokura, T. Miyazaki, and A. Tsukamoto: Sensor-less Force Control Using Resonance Ratio Control and Friction Free Reaction Force Observer, Proceedings of IEEJ SAMCON 5, TT5 3 3, pp. 6 (5) ( 5) T. Yoshioka, T.T. Phuong, K. Ohishi, T. Miyazaki, and Y. Yokokura: Variable Noise-Covariance Kalman Filter based Instantaneous State Observer for Industrial Robot, Proceedings of IEEE ICM 5, pp (5) (6) S. Manabe: Importance of Coefficient Diagram in Polynomial Method, Proceedings of IEEE CDC 3, Vol.4, pp (3) app. Fig.. Numerical simulation results under bandwidth limitation of force sensor Takashi Yoshioka (Student Member) received the B.S. and M.S. degrees, in electrical, electronics and information engineering from Nagaoka University of Technology, Niigata, Japan, in and, respectively. Now he is a candidate of Ph.D. degree in energy and environment science from Nagaoka University of Technology, Niigata, Japan. His research interests include motion control and robotics. He is a student member of the IEEE Industrial Electronics Society (IEEE IES), the Institute of Electrical Engineers of Japan (IEEJ). Akinori Yabuki (Student Member) received B.S. degree, in electrical, electronics and information engineering from Nagaoka University of Technology, Niigata, Japan, in 4. Now he is a candidate of the M.S. degree in electrical, electronics and information engineering from Nagaoka University of Technology, Niigata, Japan. His research interests include motion control and robotics. He is a student member of the IEEE Industrial Electronics Society (IEEE IES), the Institute of Electrical Engineers of Japan (IEEJ). Yuki Yokokura (Member) received his B.E. and M.E. degrees in electrical engineering from the Nagaoka University of Technology, Niigata, Japan, in 7 and 9, respectively. He received his Ph.D. degree in integrated design engineering from Keio University, Yokohama, Japan, in. From to, he was a JSPS research fellow (DC and PD). Since, he was a visiting fellow at Keio University, and a postdoctoral fellow at Nagaoka University of Technology. Since, he has been with Nagaoka University of Technology. His research interests include motion control, motor drive, power electronics, and real-world haptics. 39 IEEJ Journal IA, Vol.5, No., 6

9 Kiyoshi Ohishi (Senior Member) received the B.S., M.S., and Ph.D. degrees, all in electrical engineering, from Keio University, Yokohama, Japan, in 98, 983, and 986, respectively. From 986 to 993, he was an Associate Professor with Osaka Institute of Technology, Osaka, Japan. From 993 to 3, he was an Associate Professor with Nagaoka University of Technology, Niigata, Japan. Since August 3, he has been a Professor at the same university. He is an administration comittee member of the IEEE Industrial Electronics Society (IEEE IES), the Institute of Electrical Engineers of Japan (IEEJ), the Japan Society of Mechanical Engineers (JSME), the Society of Instrument and Control Engineers (SICE), and the Robotics Society of Japan (RSJ). Thao Tran Phuong (Member) received B.S. degree in mechatronics engineering from University of Technical Education, Ho Chi Minh city, Viet Nam in 7, M.S. degree in electrical, electronics and information engineering and Ph.D. degree in energy and environment science from Nagaoka University of Technology, Japan in and 3, respectively. At present, she is a Postdoctoral researcher at Nagaoka University of Technology, Japan. Her interests include motion control, robotics, embedded system, especially human support applications. She is a member of the IEEE Industrial Electronics Society (IEEE IES), the Institute of Electrical Engineers of Japan (IEEJ). Toshimasa Miyazaki (Member) received the B.S., M.S., and Ph.D. degrees, all in electrical engineering, from Nagaoka University of Technology, Niigata, Japan, in 994, 996, and 999, respectively. From 999 to 9, he was an Associate Professor with Nagaoka National College of Technology, Niigata, Japan. Since, he has been an Associate Professor with Nagaoka University of Technology, Niigata, Japan. His research interests include motion control and power electronics. He is a member of the IEEE Industrial Electronics Society (IEEE IES), the Institute of Electrical Engineers of Japan (IEEJ), and the Society of Instrument and Control Engineers (SICE). 4 IEEJ Journal IA, Vol.5, No., 6