Iterative Learning Motion Control for Pneumatic Artificial Muscle-Actuated Wrist Joints With Error Constraints and Nonrepetitive Tasks.

Because of the similar structures and motion mechanisms between pneumatic artificial muscles (PAMs) and biological muscles, PAM-actuated wrist joints exhibit substantial potential for applications in rehabilitation and industrial fields. Nevertheless, the complex nonlinearities, hysteresis, and creep inherent in PAMs bring significant challenges to precise motion control. Moreover, the material compliance and limited motion ranges of PAMs make it difficult to guarantee the safe operation of multi-PAM-actuated robots. To this end, a new iterative learning control (ILC) method is investigated to accomplish the efficient tracking control of PAM-actuated wrist joints. Without complex modeling work, ILC is introduced to estimate iteration-invariant dynamic model parameters. In particular, the identical initial condition in traditional ILC is relaxed by implementing a trajectory reconstruction mechanism. Also, a tanh-type adaptive law is designed to estimate the upper bounds of the lumped disturbances without prior knowledge. Moreover, an asymmetric time-varying barrier Lyapunov function (BLF) is utilized, ensuring that the tracking errors are maintained within permissible limits. The convergence of the proposed control method is demonstrated in both the time and iteration domains. Eventually, the effectiveness and superiority of the control method are validated via experiments.