DSC-based RBF neural network control for nonlinear time-delay systems with time-varying full state constraints. (October 2022)

• Record Type: Journal Article
• Authors: He, Youguo; Zhou, Yu; Cai, Yingfeng; Yuan, Chaochun; Shen, Jie
• Is Part Of: ISA transactions. Volume 129(2022)Part A
• Page Start: 79
• Page End: 90
• Publication Date: 2022-10
• Subjects: Neural network -- Barrier Lyapunov functions -- Time-varying full state constraints -- Dynamic surface control -- Time-delay systems; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.12.010
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The presented control scheme in this paper aims at stabilizing uncertain time-delayed systems requiring all states to change within the preset time-varying constraints. The controller design framework is based on the backstepping method, drastically simplified by the dynamic surface control technique. Meanwhile, the radius basis function neural networks are utilized to deal with the unknown items. To prevent all state variables from violating time-varying predefined regions, we employ the time-varying barrier Lyapunov functions during the backstepping procedure. Moreover, appropriate Lyapunov–Krasovskii functionals are used to cancel the influence of the time-delay terms on the system's stability. Under the presented control laws and Lyapunov analysis, it is proven that constraints on all state variables are not breached, good tracking performance of desired output is achieved, and all signals in the closed-loop systems are bounded. The effectiveness of our control scheme is confirmed by a simulation example. Highlights: The controller design for time-delay system with time-varying full state constraints is achieved. The neural network is used to estimate the uncertainties. DSC method was employed to simplify the design and reduce the input of NNs. More flexible inequality is used to handle time delays.