Feedforward Iterative Learning Control Compensated Fractional‐Order Model‐Free Adaptive Control

Abstract

Model‐free adaptive control (MFAC) can carry out various tasks using only input and output (I/O) data, providing advantages such as lower operational costs, higher scalability, and easier implementation. In this article, to further improve tracking performance, a fractional‐order model‐free adaptive control (FOMFAC) with P‐type feedforward iterative learning controller for uncertain discrete‐time nonlinear systems is proposed. First, the FOMFAC law is designed by using an optimization input criterion function based on a fractional‐order (FO) equivalent model, which contains more input and output data information. Then, an iterative learning control (ILC) scheme is proposed as the feedforward component term to achieve fast convergence. Different from the existing plug‐in method, the stability of the whole closed‐loop control system is rigorously proved. Finally, the presented simulation evidence substantiates both the theoretical validity and practical superiority of the novel control method.