A Novel Fast Error Convergence Approach for an Optimal Iterative Learning Controller

Design an optimized iterative learning control for linear and nonlinear dynamical systems is a challenging task. Norm-optimal iterative learning control (NOILC) is a valuable criterion for these dynamical systems. An iterative learning control algorithm based on optimal control theory is proposed, and the stability and convergence conditions of the proposed control algorithm are analyzed by using the convergence conditions of iterative learning control, and the control design is carried out based on feedforward and feedback control structure. At the same time, by introducing a weighted matrix coefficient to the feedforward control action, the convergence speed of iterative learning control algorithm based on optimal control theory is improved, and it is applied to the Matlab simulation control system. The results show that the convergence effect of the basic optimal control theory and the iterative learning control algorithm based on the weighted matrix coefficient is significant and the performance of the trajectory tracking is improved. The numerical example simulated on MATLAB@2019 and mollified results confirm the validation of the designed algorithm.