Finite‐iteration model‐free adaptive iterative learning control for a class of nonlinear switched systems

Abstract For a class of nonlinear switched systems with unknown dynamics, a model‐free adaptive iterative learning control algorithm with finite‐iteration convergence is proposed. To address the challenge of unknown system models, a dynamic linearization method is employed to derive the data‐driven relationships for the nonlinear switched system. A novel finite‐iteration convergence criterion is defined, and a performance index function incorporating fractional powers of tracking errors is proposed. Based on this, a model‐free adaptive iterative learning control algorithm is established. The convergence of the system output is analyzed using the average dwell time approach, and the finite‐iteration convergence of the tracking error is rigorously proven. Numerical simulations verify the theoretical analysis, and the results demonstrate that the proposed method achieves faster convergence compared to existing methods.