Computationally Efficient Iterative Learning Control Design for Uncertain Nonlinear Systems Applied on SRVO2 Motor-Driven Machine

This paper presents Iterative Learning Control (ILC), an intelligent control technique that addresses tracking inaccuracies in iterative systems. In this work, we propose a new iterative learning sliding mode controller ILSMC and ILPID to solve the trajectory tracking problem of nonlinear systems with model uncertainties and disturbances. The central piece of the controller signal is repeatedly generated due to the use of integrated action in combination with computational second-order sliding mode. The proposed ILSMC has a stronger noise and disturbance rejection capability compared to ILPID and the conventional sliding mode control with saturation interpolation. The design is then applied to the SRVO2 motor-driven machine. The simulation results and experimental validation have shown the significant benefits of the ILSMC control scheme over ILPID approaches upon the errors located from previous activity, transient responses are to be improved.