Convergence Analysis of Iterative Learning Control for Stocastic Semilinear Generalized Distributed Parameter Systems

The iterative learning control problem of generalized distributed parameter systems is one of the important issues in the research of generalized distributed parameter systems. In this paper convergence analysis of P-type iterative learning control for stochastic semilinear generalized distributed parameter systems is discussed in the sense of integral solution by GE-semigroup theory in separable Hilbert spaces. Firstly, the basic concepts of GE-semigroup and integral solution are presented, along with a description of the iterative learning control problem for stochastic semilinear generalized distributed parameter systems under P-type learning law. Secondly, some sufficient conditions are obtained, by which the input tracking and output tracking errors can converge to be bounded in the mean square sense. At the same time, we have also obtained sufficient conditions for the input tracking and output tracking errors of the semilinear generalized distributed parameter system to converge uniformly to zero.