Model-free optimal robust tracking control for the joint actuated by pneumatic artificial muscles with saturated air pressure input

Abstract

At present, positioning and tracking the joint angle of the PAM-actuated system is still a challenging issue, due to the inherent hysteresis, high nonlinearities, and time-varying characteristics. Considering the problem of uncertain parameters, external disturbance, and saturated input, a model-free optimal robust tracking control scheme is proposed for the PAMs system in this paper. Also, a nonquadratic cost function is introduced to address the saturation air pressure input. Moreover, an integral reinforcement learning(IRL) algorithm is developed to solve the H∞ tracking Hamilton–Jacobi–Isaacs(HJI) equation. And the IRL algorithm is implemented on a critic–actor-disturbance neural network (NN) structure. The presented control scheme avoids the complete system dynamic and simultaneously compensates for the uncertain parameters, and external disturbance. Then, the effectiveness of the optimal robust tracking controller is verified via computational simulation and keeps the system stable. In addition, the tracking experiment is implemented on a hardware platform, indicating that the presented approach achieves satisfactory tracking performance.