Applications of UKF and EnKF to estimation of contraction ratio of McKibben pneumatic artificial muscles

This study applies the unscented Kalman filter (UKF) and the ensemble Kalman filter (EnKF) to estimate a contraction ratio of the McKibben pneumatic artificial muscle (PAM) and to present that the UKF is more effective than the EnKF in estimating the PAM length. Both filters were applied for a commercial PAM, FESTO DMSP-20-200N, to validate them. The estimation was conducted offline under three types of sinusoidal control signals, which are a voltage inputted to a proportional directional control valve. The inner pressure in those cases travels in low, high, and whole ranges between 0.2 and 0.7 [MPa] respectively. An alternative technique of measuring the length using a sensor device is important in making full use of the PAM's merit.

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

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

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

[4]  Ren Wang,et al.  Interactive multiple model ensemble Kalman filter for traffic estimation and incident detection , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[6]  Geir Hovland,et al.  EKF-based estimation and control of electric drivetrain in offshore pipe racking machine , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

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

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

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

[10]  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..

[11]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[12]  Somnath Sengupta,et al.  Estimation of instantaneous states of an SI gasoline engine using EKF and UKF , 2011, 2011 Annual IEEE India Conference.

[13]  Alexandre M. Bayen,et al.  State Estimation for Polyhedral Hybrid Systems and Applications to the Godunov Scheme for Highway Traffic Estimation , 2015, IEEE Trans. Autom. Control..

[14]  Pierre Lopez,et al.  Modeling and control of McKibben artificial muscle robot actuators , 2000 .

[15]  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.

[16]  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).

[17]  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).

[18]  Ming Ding,et al.  Pinpointed muscle force control using a power-assisting device: System configuration and experiment , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[19]  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).

[20]  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.

[21]  Toshiharu Kagawa,et al.  Precise Positioning of Pneumatic Artificial Muscle Systems , 2014 .

[22]  Shuichi Wakimoto,et al.  Development of intelligent McKibben actuator , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Biao Huang,et al.  Comparative Study of State Estimation of Fuel Cell Hybrid System Using UKF and EKF , 2007, 2007 IEEE International Conference on Control and Automation.

[24]  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..

[25]  Smita Sadhu,et al.  Fault detection and identification of non-linear hybrid system using self-switched sigma point filter bank , 2015 .

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

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

[28]  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.