Adaptive Neural Network Tracking Control for Nonstrict-Feedback Nonlinear Systems With States and Inputs Quantization
针对同时存在状态和输入量化的非严格反馈非线性系统，提出一种自适应神经网络控制方法，解决虚拟控制信号无法由量化状态定义等难题，并通过仿真验证有效性。
It is a common control issue that the input signal of the system is quantized in the controller-to-actuator channel via the communication network, but few results are available in considering adaptive tracking control problem for nonstrict-feedback nonlinear system with both state and input quantization. The control problem is figured out in our article by developing an adaptive neural network control method for nonstrict-feedback nonlinear system with quantized input and states. In addition to overcoming the difficulty that the virtual control signal cannot be defined by quantized states in backstepping-based design approach, our work also surmounts the influence of the coexistence of nonstrict-feedback structure and state discontinuity resulted from quantization, and gives the construction of adaptive law for the weight vector of approximation system based on neural network. Elaborate simulation examples are depicted to verify the effectiveness of our depicted quantized control algorithm.