Adaptive neural-network-based tracking control strategy of nonlinear switched non-lower triangular systems with unmodeled dynamics

In this paper, an adaptive neural-network-based tracking control strategy is proposed for a class of nonlinear switched non-lower triangular systems with the completely unknown nonlinearities and unmodeled dynamics. The design difficulties arise mainly from the fact that the intercoupling between the non-lower triangular functions and the unmodeled dynamics leads the switched system under consideration has a very complex structure. In order to get the desired adaptive state feedback controller, a mild assumption associated with a dynamic signal is utilized to deal with the unmodeled dynamics, and a separating variable method is presented to handle the system nonlinearities with all state variables in the framework of adaptive backstepping technique, respectively. The obtained results show that all signals of the switched closed-loop system are semi-global bounded with the output tracking error can be guaranteed to enter a small region around the origin. In the end, two simulation examples are given to demonstrate the feasibility and practicability of the presented design strategy.