Iterative Feedback Tuning-based Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle

Abstract

Iterative feedback tuning (IFT) method is a data-driven control method, which can tune the parameters of the system controller without knowing the system model. Pneumatic artificial muscles (PAMs) are flexible actuators that are widely used in the field of rehabilitation robots because of their flexibility and light weight. However, its nonlinearity, difficult modeling and time-varying parameters make it difficult to control. In this paper, a model-free adaptive iterative learning control (MFAILC) method based on IFT is proposed for a strong nonlinear system such as PAM. The method obtains the dynamic linearization model of PAM behavior according to the dynamic linearization theorem, then designs the controller structure, and finally uses the IFT method to optimize the controller parameters. The method proposed in this paper was compared with the MFAILC method. The simulation results show that the proposed method has a faster convergence speed and smaller tracking errors in the desired trajectory tracking control, and its effectiveness is also verified.