Hybrid-based model-free iterative learning control with optimal performance

In this paper, a hybrid-based model-free iterative learning control algorithm is proposed to improve the robustness and convergence speed of model-free iterative learning control in noisy environments. The proposed algorithm divides the iterative process into a rapidly decreasing error phase and an error convergence phase, and uses different control algorithms in different phases, thus combining different advantages of the original algorithms. In addition to this, this work proves the convergence and robustness of the proposed algorithm and summarises the design idea of this controller. Finally, the convergence performance of the algorithm in noisy environments and in variable reference trajectory environment is simulated to demonstrate the effectiveness of the algorithm proposed in this work.