The Analysis of Convergence Speed for an Open and Closed Loop Second Order Iterative Learning Control Algorithm

An open-closed-loop second-order iterative learning control algorithm is investigated in the presence of the system uncertainties. Through the rigorous analysis, it is obtained that this algorithm speeds up the convergence rate of iterative learning control in the iteration domain because the closed-loop learning gain is introduced based on an open-loop iterative learning control algorithm. It is proven that this algorithm converges faster than an open-loop iterative learning control algorithm by means of a robust optimization method. Moreover, the convergence rate of the iterative learning control algorithm can be significantly accelerated by the higher closed-loop learning gain. The simulation results for a nonlinear system show the effectiveness of the algorithm