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

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.