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.

中文翻译：

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基于神经网络的输入时滞非线性约束系统的自适应控制

本文针对一类具有输入时滞的严格反馈非线性状态约束系统，提出了自适应跟踪控制的问题。为了缓解因出现全状态约束和输入延迟而引起的主要挑战，使用了适当的势垒Lyapunov函数和适当的反推设计来避免约束违反，并采用Pade逼近和中间变量来消除约束的影响。输入延迟。在设计过程中，使用神经网络来估计未知功能。事实证明，闭环信号最终是半全局一致有界的，并且跟踪误差收敛到原点的紧凑集合，并且状态保持在有界区间内。