Adaptive neural control for switched non-linear systems with multiple tracking error constraints

Here, an adaptive neural control problem for a switched non-linear system with multiple tracking error constraints is studied by using the dwell-time method. The unknown functions are approximated by radial basis function neural networks. In order to avoid the difficulty caused by the adoption of different coordinate transformations, a common transition function is selected. Moreover, different update laws are designed for both active time-interval and inactive time-interval of each subsystem. The proposed controllers and switching signals guarantee the stability of the closed-loop system and the boundedness of all signals. Furthermore, both transient-state and steady-state performances of the tracking errors are ensured. Finally, a simulation example is used to clarify the effectiveness of the proposed method.