Parameter Estimation of McKibben Pneumatic Artificial Muscle System by Game-theoretic Learning

This paper discusses about estimating nine parameters of a nonlinear model of a McKibben pneumatic artificial muscle (PAM) system in a game-theoretic learning way. An algorithm for the learning is constructed to find a Nash equilibrium in which the parameters induce transient and output responses close to corresponding experimental reference data. With a practical PAM system we made for validation, then, it is confirmed that the learning algorithm can estimate better parameter values in the sense of how close the computed responses and the reference data are and of how long it takes to acquire the parameter values.

[1]  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.

[2]  Jiping He,et al.  Characterization of the Dynamic Properties of Pneumatic Muscle Actuators , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

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

[4]  Kenji Sugimoto,et al.  Identification procedure for McKibben pneumatic artificial muscle systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  T. Tjahjowidodo,et al.  A New Approach to Modeling Hysteresis in a Pneumatic Artificial Muscle Using The Maxwell-Slip Model , 2011, IEEE/ASME Transactions on Mechatronics.

[6]  李 向攀.  Design of Wearable Power Assist Wear for Low Back Support Using Pneumatic Actuators , 2013 .

[7]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[8]  K. Tadano,et al.  Achieving Haptic Perception in Forceps’ Manipulator Using Pneumatic Artificial Muscle , 2013, IEEE/ASME Transactions on Mechatronics.

[9]  Blake Hannaford,et al.  Measurement and modeling of McKibben pneumatic artificial muscles , 1996, IEEE Trans. Robotics Autom..

[10]  Toshiro Noritsugu,et al.  Application of rubber artificial muscle manipulator as a rehabilitation robot , 1997 .

[11]  Nikolaos G. Tsagarakis,et al.  Development and Control of a ‘Soft-Actuated’ Exoskeleton for Use in Physiotherapy and Training , 2003, Auton. Robots.

[12]  Yildirim Hurmuzlu,et al.  A High Performance Pneumatic Force Actuator System: Part I—Nonlinear Mathematical Model , 2000 .

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

[14]  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).