Tracking Control for Nonlinear Nonparametric Systems Based on the Stochastic Approximation Algorithm

Abstract

In this article, we study the tracking control problem for nonlinear nonparametric systems, and additive random observation noise is also taken into account. The dynamical function is allowed to be time varying and possess an arbitrary growth rate at control input. The control algorithm is designed on the basis of the stochastic approximation algorithm with expanding truncations, and it is found that there is a tradeoff between the growth rate of the dynamical function at control input and that of the truncation bound sequence to be chosen. We prove that the average tracking or strong tracking is asymptotically achieved for a class of reference state sequences, which can be strongly averaged. Finally, numerical simulations given in this article justify the theoretical assertions.