LMI based nonlinear Iterative Learning Control for uncertain discrete repetitive system

The problem encountered in this paper is to design a robust, feedback-based improved control system for the plant that involves systematic uncertainty. This paper proposes a fault estimation algorithm based on iterative learning control. This algorithm is constructed through an optimization function to prove the robustness and convergence of the algorithm. Through linear matrix inequality (LMI), the observer gain matrix and iterative learning parameter matrix in the algorithm are solved. The two comprehensive parameters in LMI represent the parameter selection in the two specifications to make selected adjustments in learning and control. A numerical example shows the improvement process and the effectiveness of these methods. Through LMI techniques, we have obtained satisfactory results and controller stability and robustness against fault-tolerant control. Lastly, the simulation results show the effectiveness and accuracy of the proposed algorithm.