Iterative Learning Tracking Control for Nonlinear Systems Based on Data Driven Control

This paper introduces a novel methodology for tracking control of general nonlinear systems. Unlike traditional methods, which rely on linearization or nonlinear cancellation. This article starts from the iterative domain and utilizes the concept of iterative learning control to improve the control performance of the system. It employs a data-driven approach for model-free adaptive control, addressing the challenges posed by strong system nonlinearity, difficulties in control, and even modeling issues caused by disturbances and other factors. It enables precise trajectory tracking without the need for complex computations, making it suitable for real-time control applications. The effectiveness of the methodology is demonstrated through examples.