A Newton-type iterative learning algorithm of output tracking control for uncertain nonlinear distributed parameter systems

A new iterative learning algorithm of output tracking control for uncertain nonlinear distributed parameter systems is considered in this paper. The iterative learning control scheme based on Newton-type method is constructed. Sufficient conditions for the convergence of this new algorithm are given. Using Green formula and the operator Taylor expansion method in Banach space, the convergence of the Newton-type iterative learning control algorithm is proved. The significant of this paper is to provide a Newton-type iterative learning control scheme with rapid convergence speed, which is a new method for solving the output tracking problem in distributed parameter systems.