Adaptive NN Dynamic Surface Controller Design for Nonlinear Pure-Feedback Switched Systems With Time-Delays and Quantized Input
针对一类具有量化输入和时滞的非线性纯反馈切换系统，提出一种自适应神经网络动态面控制方法，利用Lyapunov-Krasovskii泛函补偿时滞，并通过径向基神经网络逼近未知函数，确保闭环系统半全局一致有界稳定。
This paper deals with the problem of robust stabilization for a class of uncertain nonlinear pure-feedback switched time-delay systems with quantized input signal. In order to overcome the design difficulty caused by the quantization and unpredictable switchings, a nonlinear decomposition strategy for quantizer is employed in advance. In the controller design procedure, the unknown time-delay terms are compensated by utilizing Lyapunov-Krasovskii functionals, the desired virtual stabilizing functions and desired actual control input are approximated by applying radial basis function neural networks, and dynamic surface control technology are used to handle the issue of “explosion of complexity” in the traditional backstepping procedure. Furthermore, it is shown that the resulting closed-loop system are stable in the sense of semi-global uniformly ultimately bounded. Finally, to verify the effectiveness and applicability of the presented control scheme, an example is given to construct an adaptive neural controller for an electromechanical system.