Approximation-based adaptive neural output feedback control for a class of uncertain switched stochastic nonlinear systems with average dwell time condition

This paper studies the problem of adaptive neural output feedback controller design for a class of uncertain switched stochastic nonlinear systems in strict-feedback form. In the design procedure, a common coordinate transformation for all subsystems is constructed to overcome the design difficulty caused by adoption of different coordinate transformation for different subsystems. Then, by using switched state observer to estimate the unmeasured states and different update laws for different subsystems, a novel neural output-feedback controller is designed via backstepping approach. Furthermore, based on the Lyapunov method and the average dwell time condition, the stability of the resulting closed-loop system can be achieved. It is shown that all the signals of the closed-loop system are bounded under a class of switching signals with average dwell time. Finally, simulation results are included for validating the advantages of the proposed approaches.