A Class of Nonlinear Iterative Learning Control with Open-Closed Loop Separation

A separation-based open-closed loop iterative learning control scheme with parameter optimization is proposed for a class of nonlinear systems with unknown parameters. First, an open-loop iterative controller with gain optimization is employed to iteratively optimize the reference trajectory into a virtual reference trajectory. Then, the virtual reference trajectory containing iterative information is fed into a closed-loop proportional (P-type) controller to achieve iterative optimization. In the proposed openclosed loop iterative learning controller, the open-loop and closedloop components are separated, and the controller structure explicitly exhibits only the closed-loop feedback function, while the open-loop iterative optimization is embedded in the update of the virtual reference trajectory. Utilizing contraction mapping as the fundamental analytical tool, the convergence of the proposed method is rigorously proven. Finally, numerical results demonstrate the effectiveness of the proposed scheme.