PID Position Control of McKibben Pneumatic Artificial Muscle Using Only Pressure Feedback

This study experimentally investigates that tracking control performance of an unscented-Kalman-filter(UKF)-based position control system for a McKibben pneumatic artificial muscle (PAM), using only a pressure sensor. Because nonlinearities in the PAM cause a mismatch in the PAM's length and inner pressure, achieving acceptable tracking performance using position control based on pressure measurement is challenging. This study employs the UKF to estimate the PAM's length using a measured inner pressure, and demonstrates the tracking performance of a UKF-based proportional-integral-derivative (PID) control system, comparing it with a position-feedback control system. This demonstration concludes that the UKF-based position control system using only a pressure sensor is practical.

[1]  George Nikolakopoulos,et al.  Piecewise Affine Modeling and Constrained Optimal Control for a Pneumatic Artificial Muscle , 2014, IEEE Transactions on Industrial Electronics.

[2]  Toshiro Noritsugu,et al.  Application of rubber artificial muscle manipulator as a rehabilitation robot , 1996, Proceedings 5th IEEE International Workshop on Robot and Human Communication. RO-MAN'96 TSUKUBA.

[3]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[4]  M. Yamakita,et al.  Comparative study of simultaneous parameter-state estimations , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[5]  G.S. Sawicki,et al.  Powered lower limb orthoses: applications in motor adaptation and rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[6]  Manukid Parnichkun,et al.  Position control of a pneumatic surgical robot using PSO based 2-DOF H∞ loop shaping structured controller , 2017 .

[7]  Oussama Khatib,et al.  A hybrid actuation approach for human-friendly robot design , 2008, 2008 IEEE International Conference on Robotics and Automation.

[8]  Daniel W. Repperger,et al.  Controller design involving gain scheduling for a large scale pneumatic muscle actuator , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[9]  Kiminao Kogiso,et al.  Applications of UKF and EnKF to estimation of contraction ratio of McKibben pneumatic artificial muscles , 2017, 2017 American Control Conference (ACC).

[10]  Daisuke Sasaki,et al.  Wearable Master-Slave Training Device for Lower Limb Constructed with Pneumatic Rubber Artificial Muscles , 2008, J. Robotics Mechatronics.

[11]  Darwin G. Caldwell,et al.  Adaptive position control of antagonistic pneumatic muscle actuators , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[12]  Takamitsu Matsubara,et al.  Pneumatic artificial muscle-driven robot control using local update reinforcement learning , 2017, Adv. Robotics.

[13]  Hongjiu Yang,et al.  Angle tracking of a pneumatic muscle actuator mechanism under varying load conditions , 2017 .

[14]  Francesco Amato,et al.  Identification and modelling of the friction-induced hysteresis in pneumatic actuators for biomimetic robots , 2014, 22nd Mediterranean Conference on Control and Automation.

[15]  George Nikolakopoulos,et al.  Adaptive Internal Model Control scheme for a Pneumatic Artificial Muscle , 2013, 2013 European Control Conference (ECC).

[16]  Marc D. Killpack,et al.  Control of a pneumatically actuated, fully inflatable, fabric-based, humanoid robot , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[17]  Scott Pardoel,et al.  Dynamic contraction behaviour of pneumatic artificial muscle , 2017 .

[18]  A. L. Morales,et al.  Dynamic behaviour of pneumatic linear actuators , 2017 .

[19]  Jun Ueda,et al.  An Asymptotically Stable Pressure Observer Based on Load and Displacement Sensing for Pneumatic Actuators With Long Transmission Lines , 2017, IEEE/ASME Transactions on Mechatronics.

[20]  Kiminao Kogiso,et al.  Hybrid nonlinear model of McKibben pneumatic artificial muscle systems incorporating a pressure-dependent Coulomb friction coefficient , 2015, 2015 IEEE Conference on Control Applications (CCA).

[21]  Kiminao Kogiso,et al.  Hybrid modeling of McKibben pneumatic artificial muscle systems , 2011, 2011 IEEE International Conference on Industrial Technology.

[22]  Daniel W. Repperger,et al.  Fuzzy PD+I learning control for a pneumatic muscle , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[23]  Kiminao Kogiso,et al.  Efficient PSO-based algorithm for parameter estimation of McKibben PAM model , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).

[24]  J. Landaluze,et al.  Modelling in Modelica and position control of a 1-DoF set-up powered by pneumatic muscles , 2010 .

[25]  Jun Morimoto,et al.  Optimal control approach for pneumatic artificial muscle with using pressure-force conversion model , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Takamitsu Matsubara,et al.  Kernel dynamic policy programming: Practical reinforcement learning for high-dimensional robots , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).

[27]  Henrique Marra Menegaz,et al.  A Systematization of the Unscented Kalman Filter Theory , 2015, IEEE Transactions on Automatic Control.

[28]  Osamu Kaneko,et al.  Data-driven tuning of nonlinear internal model controllers for pneumatic artificial muscles , 2014, 2014 4th Australian Control Conference (AUCC).

[29]  B. Tondu,et al.  Closed-loop position control of artificial muscles with a single integral action: Application to robust positioning of McKibben artificial muscle , 2013, 2013 IEEE International Conference on Mechatronics (ICM).

[30]  Hendrik Van Brussel,et al.  Cascade position control of a single pneumatic artificial muscle-mass system with hysteresis compensation , 2010 .