Adaptive tracking control for a class of non-affine switched stochastic nonlinear systems with unmodeled dynamics

Abstract

This paper proposes an adaptive tracking control scheme for a class of non-affine switched stochastic nonlinear systems with unmeasured states and stochastic inverse dynamics. K-filters are used to estimate unmeasured states, and a changing supply function is introduced to deal with stochastic inverse dynamics. By using neural networks, dynamic surface technique and common Lyapunov function method, a controller and adaptive laws are designed for the considered system. The proposed control scheme guarantees that all signals of the closed-loop system are bounded in probability. Finally, a simulation example is presented to show the effectiveness of the proposed method.

Adaptive Output Feedback Stabilization of Random Nonlinear Systems with Unmodeled Dynamics Driven by Colored Noise

“…In [17], a unifying solution to stochastic adaptive output feedback stabilization was presented by introducing dynamic signal and changing supply function technique. Also, some other control strategies such as adaptive neural networks control [18][19][20], adaptive fuzzy control [21,22], and adaptive tracking control [23] were proposed.…”