Adaptive neural tracking control for switched nonlinear systems with state quantization q

In this study, an adaptive neural tracking control problem for uncertain switched nonlinear systems with state quantization under arbitrary switching is investigated. A command-filtered backstepping control design strategy is implemented to overcome the difficulty that the time derivate of common virtual con-trol signals cannot be well defined. By deriving the closed-loop system quantized errors bounded, quan-tized states can be used to the control design and unquantized states can be applied to the stability analysis. And then, an adaptive neural tracking control approach for switched nonlinear systems with state quantization via common Lyapunov function is proposed, which guarantees that all the signals in the closed-loop system remain semiglobal uniform ultimate boundedness and the genuine output of sys-tem can well track the reference trajectory. Finally, the proposed method is demonstrated by two simu-lation results. (c) 2021 Published by Elsevier B.V.