An iterative learning control method based on a Kalman filter

Abstract

A forgetting factor iterative learning control law based on a Kalman filter is proposed to solve the problem whereby the dynamic performance of a class of nonlinear underactuated mechanical systems will deteriorate in a disturbance environment in order to realize the stable control and interference suppression in the closed-loop system. The Kalman filter is used as the state observer to estimate the optimal states of the system with Gaussian noise. The adaptive forgetting factor is then set to dynamically adapt to the error variation in the iterative learning process so that the system can track the reference trajectory accurately and quickly. Finally, the flexible ruler produced by the Quanser Company is used as the experimental platform to study the control method of the actual system of the nonlinear underactuated controlled object. The proposed method has been verified by theoretical numerical simulation and real experiments. Simulation and real experiment results show that the proposed control method can ensure the stable operation of the system, and the system can also maintain good robustness when random noise exists in the environment.