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Adaptive neural tracking control for nonlinear switched systems with dynamic uncertainties

This paper investigates the problem of adaptive approximation-based neural tracking control strategy for a class of switched non-lower triangular nonlinear systems with dynamic uncertainties. The design hardships, which exist in unmodeled dynamics and non-lower triangular form, are tackled by utilizing a dynamic signal and a variable partition strategy for the nonlinear functions with all state variables, respectively. The obtained result shows that all signals of the closed-loop switched system are semi-global bounded with the designed controller and the system output can be guaranteed to enter a small region around the origin.