High-order iterative learning control for nonlinear systems

Iterative learning control demands the same initial state in each iteration, which is equal to the desired state. But this condition is unattainable in practice. This paper addresses the problem of some fixed initial state in iterative learning control for high-order nonlinear system. It presents a new control algorithm. In the process of tracking, this algorithm can rectify the initial errors through a step-by-step rectifying controller. The controller rectifies the xn at first, then xn−1 after finishing the rectifying actions of xn, and so on. All of these rectifying actions are finished in a small interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.