具有事件触发机制的非线性容错控制系统的自适应模糊 ABLF 函数固定时间跟踪控制方法

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-07-22 DOI:10.1002/acs.3873
Guodong You, Xingyun Li, Jinyuan Wu, Bin Xu, Leijiao Ge, Zhifang Shen, Hailong Zhang
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引用次数: 0

摘要

摘要 随着许多实际物理控制系统的非线性越来越强,它们难以用精确的数学模型来描述。针对一类具有全状态约束和执行器故障的非线性单输入单输出系统,提出了一种事件触发自适应模糊 ABLF 函数定时跟踪控制方法。非对称障碍李亚普诺夫函数(ABLF)与后退阶跃技术相结合,确保了闭环系统输出的有界性。为了解决带宽资源有限的问题,本文采用了事件触发机制和模糊控制技术来逼近未知函数,以确保系统在固定时间内收敛。经过理论分析,证明系统的跟踪误差在原点的小邻域内收敛,且在执行器发生故障时仍具有良好的跟踪性能。最后,通过将所提方法与非对称屏障 Lyapunov 函数进行比较,验证了所提方法的有效性。
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Adaptive fuzzy ABLF function fixed-time tracking control method for nonlinear fault-tolerant control systems with event triggering mechanism

As the nonlinearities of many real physical controlled systems become stronger and stronger, they are difficult to be described by accurate mathematical models. For a class of nonlinear single-input single-output systems with all-state constraints and actuator faults, an event-triggered adaptive fuzzy ABLF function fixed-time tracking control method is proposed. The asymmetric barrier Lyapunov function (ABLF) combined with backward step technique is used to ensure the boundedness of the closed-loop system output. In order to solve the problem of limited bandwidth resources, this paper adopts the event trigger mechanism and fuzzy control technology to approximate the unknown function to ensure the convergence of the system in a fixed time. After theoretical analysis, it is proved that the tracking error of the system converges in the small neighborhood of the origin, and it still has good tracking performance when the actuator faults. Finally, by comparing the proposed method with the asymmetric barrier Lyapunov function, which verifies the effectiveness of the proposed method.

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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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