Predictor-Based Fixed-Time Neural Dynamics Surface Tracking Control for Nonlinear Systems With Unknown Backlash-Like Hysteresis
针对含未知齿隙类滞回的非线性系统，提出一种基于预测器的神经固定时间动态面控制方法，通过改进动态面降低设计难度，确保系统信号在固定时间内有界。
The issue of predictor-based neural fixed-time dynamic surface control for the nonlinear systems with unknown backlash-like hysteresis is the research focus of this article. By applying the predictor-based neural control scheme, the system nonlinear functions can be smoothly estimated. In addition, an improved dynamics surface is proposed to decrease the difficulty of the controller design procedure while ensuring that the dynamic surface compensating signals can satisfy the fixed-time stability. Further, on the basis of fixed-time theorem and backstepping control technology, the designed controller can ensure all signals of the considered closed-loop systems are fixed-time bounded in the presence of unknown backlash-like hysteresis. Eventually, the simulation cases are given to imply the effectiveness of the designed method.