Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay
针对一类带有全状态约束和输入延迟的严格反馈非线性系统，提出一种基于神经网络的自适应跟踪控制方法，利用障碍李雅普诺夫函数和Pade近似保证状态不越界并消除延迟影响。
This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.