Robust Control for an Artificial Muscles Robot Arm

We are concerned with the control of a 3-DOF robot arm actuated by pneumatic rubber muscles. The system is highly non-linear and somehow difficult to model therefore resorting to robust control is required.The work in this paper addresses this problem by presenting two types of robust control. One uses neural network control, which has powerful learning capability, adaptation and tackles nonlinearities; in our work the learning performed on-line is based on a binary reinforcement signal without knowing the nonlinearities appearing in the system and no preliminary off-line learning phase is required. The other control law is a Classical variable structure which is robust against parameters variations and external disturbances. Experimental results together with a comparative study are presented and discussed.

[1]  Kyoung Kwan Ahn,et al.  Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network , 2006 .

[2]  Frank L. Lewis,et al.  Direct-reinforcement-adaptive-learning neural network control for nonlinear systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[3]  Fumio Harashima,et al.  Practical robust control of robot arm using variable structure system , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[4]  G.A. Medrano-Cerda,et al.  Braided pneumatic actuator control of a multi-jointed manipulator , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[5]  Marios M. Polycarpou,et al.  High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.

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

[7]  Nader Sadegh,et al.  A perceptron network for functional identification and control of nonlinear systems , 1993, IEEE Trans. Neural Networks.

[8]  Dawei Cai,et al.  A VSS control method for a manipulator driven by an artificial muscle actuator , 1998 .

[9]  John Y. Hung,et al.  Variable structure control: a survey , 1993, IEEE Trans. Ind. Electron..

[10]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[11]  K. Narendra,et al.  A New Adaptive Law for Robust Adaptation without Persistent Excitation , 1986, 1986 American Control Conference.

[12]  Marek Perkowski,et al.  Adaptive Reflex Control for an Artificial Hand , 2003 .

[13]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[14]  G CaldwellD,et al.  Braided Pneumatic Actuator Control of a Multi-Jointed Manipulator. , 1993 .

[15]  Vadim I. Utkin,et al.  A control engineer's guide to sliding mode control , 1999, IEEE Trans. Control. Syst. Technol..

[16]  Kuldip S. Rattan,et al.  Feedforward control of a non-linear pneumatic muscle system using fuzzy logic , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[17]  Pablo Carbonell,et al.  Nonlinear control of a pneumatic muscle actuator: backstepping vs. sliding-mode , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[18]  Frank L. Lewis,et al.  Neural net robot controller with guaranteed tracking performance , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[19]  Mustapha Hamerlain,et al.  An anthropomorphic robot arm driven by artificial muscles using a variable structure control , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.