Simultaneous Estimation of Contraction Ratio and Parameter of McKibben Pneumatic Artificial Muscle Model Using Log-Normalized Unscented Kalman Filter

Using an unscented Kalman filter (UKF), the paper estimates the contraction ratio and unknown parameters in a mathematical model of McKibben pneumatic artificial muscle. In an application study, the UKF effectively estimated the contraction ratio and the unknown parameters at various the measured pressures.

[1]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

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

[3]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[4]  Shuichi Adachi,et al.  Simultaneous state of charge and parameter estimation of lithium-ion battery using log-normalized unscented Kalman Filter , 2015, 2015 American Control Conference (ACC).

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

[6]  Wojciech Lepiarz The vision analysis of a McKibben pneumatic artificial muscle , 2014, Proceedings of the 2014 15th International Carpathian Control Conference (ICCC).

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

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

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

[10]  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).

[11]  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.