具有状态约束和致动器滞后的非线性时延系统的有限时间规定性能跟踪控制

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2024-07-24 DOI:10.1016/j.isatra.2024.07.027

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引用次数: 0

摘要

本文研究了一类受全状态约束的非严格反馈时延系统的自适应神经网络规定性能跟踪控制问题。将径向基函数（RBF）神经网络（NNs）集成到反步进介质中以处理不确定函数，而障碍李亚普诺夫函数（BLF）技术则确保系统状态不超过其极限。随后，结合 Lyapunov-Krasovskii 函数，所提出的控制方案使跟踪误差收敛到预设区域，同时不违反状态约束。最后，两个仿真实验证明了该方案的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Finite-time prescribed performance tracking control for nonlinear time-delay systems with state constraints and actuator hysteresis

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov–Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments.

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.