Event-triggered adaptive neural-network control of nonlinear MIMO systems

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-28 DOI:10.1002/acs.3814

Yuelei Yu, Wenshan Bi, Shuai Sui, C. L. Philip Chen

{"title":"Event-triggered adaptive neural-network control of nonlinear MIMO systems","authors":"Yuelei Yu, Wenshan Bi, Shuai Sui, C. L. Philip Chen","doi":"10.1002/acs.3814","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This article investigates an adaptive neural networks (NNs) tracking control design issue for nonlinear multi-input and multi-output (MIMO) systems involving the sensor-to-controller event-triggered mechanism (ETM). In the design, NNs are utilized to approximate the unknown nonlinear functions. A sensor-to-controller ETM is designed to save unnecessary transmission and communication resources. Subsequently, a first-order filter technique is presented to solve the problem that the virtual control function is not differentiable. Furthermore, an event-triggered adaptive NNs control strategy is presented by constructing Lyapunov functions and using adaptive backstepping recursive design. It is demonstrated that the presented scheme can ensure the whole closed-loop signals are uniformly ultimately bounded without exhibiting the Zeno behavior. Finally, a numerical simulation example confirms the effectiveness of the presented adaptive event-triggered control (ETC) approach.</p>\n </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Adaptive Control and Signal Processing","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acs.3814","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

This article investigates an adaptive neural networks (NNs) tracking control design issue for nonlinear multi-input and multi-output (MIMO) systems involving the sensor-to-controller event-triggered mechanism (ETM). In the design, NNs are utilized to approximate the unknown nonlinear functions. A sensor-to-controller ETM is designed to save unnecessary transmission and communication resources. Subsequently, a first-order filter technique is presented to solve the problem that the virtual control function is not differentiable. Furthermore, an event-triggered adaptive NNs control strategy is presented by constructing Lyapunov functions and using adaptive backstepping recursive design. It is demonstrated that the presented scheme can ensure the whole closed-loop signals are uniformly ultimately bounded without exhibiting the Zeno behavior. Finally, a numerical simulation example confirms the effectiveness of the presented adaptive event-triggered control (ETC) approach.

查看原文本刊更多论文

非线性多输入多输出系统的事件触发自适应神经网络控制

摘要本文研究了涉及传感器到控制器事件触发机制（ETM）的非线性多输入多输出（MIMO）系统的自适应神经网络（NNs）跟踪控制设计问题。在设计中，利用 NN 近似未知的非线性函数。设计传感器到控制器的 ETM 是为了节省不必要的传输和通信资源。随后，提出了一种一阶滤波技术来解决虚拟控制函数不可微的问题。此外，通过构建 Lyapunov 函数和使用自适应反步递归设计，提出了一种事件触发自适应 NN 控制策略。结果表明，所提出的方案能确保整个闭环信号均匀地最终受限，而不会出现芝诺行为。最后，一个数值模拟实例证实了所提出的自适应事件触发控制（ETC）方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

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.