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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.