Data-Driven High-Order Fully Actuated Iterative Learning Control for Unknown Nonaffine Nonlinear Systems

This paper aims to investigate iterative learning control based on the high-order fully actuated (HOFA) system method. Firstly, the discrete-time nonaffine nonlinear HOFA system is transformed into an equivalent representation along the iterative direction, which includes an unknown parameter compressed into all unknown nonlinearities. Then, the parameter is estimated iteratively by a projection algorithm. Based on this, a data-driven HOFA iterative learning control (DD-HOFAILC) method is proposed under the HOFA system framework. Different from the traditional HOFA system methods, the proposed DD-HOFAILC is a data-driven control approach, where the model information of the system is not used. In addition, this work combines the HOFA system method with data-driven ILC, which further enriches the theory of HOFA systems. Finally, rigorous theoretical analysis is provided using mathematical tools such as matrix theory and mathematical induction, and simulation results verify the effectiveness of the proposed DD-HOFAILC method.