Abstract: Pneumatic muscle actuator (PMA) is arrested attention recently. Its main advantage is the high efficiency that it is possible to obtain from a relatively light device. However, the problems with control and compliance of pneumatic system prevent its widespread use, and the nonlinearity in the system limits its controllability. A CAMC-based variable structure position controller is proposed based on the dynamic model. The control scheme comprises a stable controller and a CMAC neural network. The CMAC neural network is employed to approximate and compensate the uncertainties induced by inaccurate modeling of the system as a feed-forward compensator. Fast tracking error convergence is obtained through CMAC. The stable controller is used to obtain the robustness under the bounded disturbances and the approximation error of CMAC. The effectiveness of the proposed control approach is demonstrated by experiment. The results are shown to be accurate, and robust to changes in payload.

Key words: CMAC, Pneumatic muscle actuators, Robustness, Variable structure control, Bootstrap, Interval estimation, MTBF, NC machine tools