Self-Triggered Adaptive NN Tracking Control for a Class of Continuous-Time Nonlinear Systems With Input Constraints
针对输入受限且动态未知的连续时间非线性系统，提出一种自触发自适应神经网络跟踪控制器，利用鲁棒精确微分器更新权重，并设计辅助补偿器处理输入约束，仿真验证了有效性。
This article develops a self-triggered adaptive neural network (NN) tracking controller for a class of continuous-time nonlinear systems, that is, input constrained and with unknown drift and input dynamics. Since the drift and input dynamics are both unknown, an NN is built within a self-triggered update paradigm to approximate the unknown tracking control. The error derivative used in the weight update algorithm is derived using a robust exact differentiator technique. To address input constraints, an auxiliary compensator is designed for the unimplemented control effort. Through rigorous Lyapunov analyses, we can guarantee that all the tracking and weight errors are uniformly ultimately bounded. Finally, to show the effectiveness of the proposed control performance, simulation results of a two-link robot are provided and analyzed.