PPMLM direct thrust force control based on iterative learning high-order improved model free adaptive control

Abstract

A high-order improved model free adaptive control method based on iterative learning is designed to address the problem that primary permanent magnet linear motor has poor control performance, susceptibilities to load disturbances and other nonlinear disturbances during operation. The proposed algorithm adopts an improved dynamic linearization model and high-order pseudo partial derivative estimation algorithm, which improves the data utilization of the data-driven control algorithm, makes the algorithm better to describe the dynamic behaviour of the primary permanent magnet linear motor direct thrust force control system and improves the speed tracking accuracy and anti-interference ability of the system. In addition, iterative learning control was adopted as feedforward compensation to further improve the control performance of the system and the stability of the closed-loop system was analysed analytically. The simulation results show that the proposed control algorithm can improve the control accuracy of the system and suppress load disturbances and other nonlinear disturbances.