This article presents an adaptive neural compensation scheme for a class of large‐scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed‐loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.

中文翻译：

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具有执行器故障和未知死区的大型时滞系统基于自适应神经控制的扰动观测器设计

本文针对存在未知死区，外部干扰和执行器故障的一类大型时滞非线性系统提出了一种自适应神经补偿方案。在本文中，介绍了二次Lyapunov–Krasovskii函数以解决系统延迟。通过使用径向基函数神经网络来估计系统的未知功能。此外，开发了干扰观测器以近似外部干扰。提出的自适应神经补偿控制方法是利用反推技术构造的。通过Lyapunov分析可确保所有闭环信号的有界性，并且跟踪误差已证明收敛于原点的一小部分。