MTN-Based Adaptive Finite-Time Tracking Control for Switched Non-Linear Systems With Time-Varying State Function Constraints

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

International Journal of Adaptive Control and Signal Processing Pub Date : 2025-01-20 DOI:10.1002/acs.3973

Jing-Jing Sun, Shan-Liang Zhu, Yao-Yao Guo, Yu-Qun Han

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Abstract

This paper studies the finite-time control problem of a class of switched non-linear systems under time-varying state constraints and proposes an adaptive finite-time controller based on a multi-dimensional Taylor network (MTN). Firstly, the time-varying tangent barrier Lyapunov functions (TBLFs) are constructed to ensure that all system states are constrained within a certain range. Secondly, MTNs are used to estimate the unknown non-linear functions during the controller design process. The proposed control scheme ensures that the tracking error of the system can converge to a small domain of the origin in a finite-time. At the same time, all the signals of the closed-loop system are semi-global practical finite-time stable (SGPFS) and all states satisfy the defined time-varying state constraints. Finally, the effectiveness of the control strategy is verified through numerical simulation examples and practical simulation examples.

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基于 MTN 的具有时变状态函数约束的开关非线性系统自适应有限时间跟踪控制

研究了时变状态约束下一类切换非线性系统的有限时间控制问题，提出了一种基于多维泰勒网络（MTN）的自适应有限时间控制器。首先，构造时变切势垒Lyapunov函数（tblf），保证系统的所有状态都被约束在一定范围内；其次，利用mtn对控制器设计过程中的未知非线性函数进行估计。所提出的控制方案保证了系统的跟踪误差在有限时间内收敛到原点的一个小范围内。同时，闭环系统的所有信号都是半全局实用有限时间稳定的（SGPFS），所有状态都满足定义的时变状态约束。最后，通过数值仿真算例和实际仿真算例验证了控制策略的有效性。

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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.