Adaptive NNs asymptotic tracking control for high-order nonlinear systems under prescribed performance and asymmetric output 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 : 2024-06-02 DOI:10.1002/acs.3858

Kun Jiang, Xuxi Zhang

引用次数: 0

Abstract

This article studies the adaptive neural networks (NNs) asymptotic tracking control of high-order nonlinear systems subject to prescribed performance, non-strict-feedback structure, and output constraints. To address the output constraint issue while guaranteeing that the tracking error stays within the specified area, a variable fused with the time-varying constraint functions is introduced. Then, a pivotal form of coordinate transformation is developed, which plays a key role in achieving asymptotic tracking performance. Based on the backstepping and Lyapunov method, the designed control scheme assures that all system variables are semi-globally uniformly ultimately bounded, the output constraints are never broken, and the tracking error always stays within the predefined function and asymptotically converges to zero. Finally, the effectiveness of theoretical findings is verified via simulation studies.

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规定性能和非对称输出约束条件下高阶非线性系统的自适应 NNs 渐近跟踪控制

本文研究了高阶非线性系统的自适应神经网络（NNs）渐近跟踪控制，该控制受制于规定性能、非严格反馈结构和输出约束。为了解决输出约束问题，同时保证跟踪误差保持在指定区域内，引入了一个与时变约束函数融合的变量。然后，开发了一种关键的坐标变换形式，它在实现渐近跟踪性能方面发挥了关键作用。基于反步法和 Lyapunov 方法，所设计的控制方案确保了所有系统变量都是半全局均匀终极约束的，输出约束从未被破坏，跟踪误差始终保持在预定函数范围内并渐进地趋近于零。最后，通过模拟研究验证了理论结论的有效性。

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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.

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