Robust State-Feedback Controller of Uncertain Systems Based on Non-Monotonic Approach

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

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-10-10 DOI:10.1002/acs.3922

Yattou El Fadili, Bensalem Boukili, Mouctar N'Diaye, Ismail Boumhidi

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引用次数: 0

Abstract

In this article, new linear matrix inequality (LMI) conditions are proposed to guarantee robust stability of the closed-loop of the linear time-invariant one-dimensional uncertain system by dealing with both continuous-time (CT) and discrete-time (DT) cases. These improved conditions for robust state feedback control combine the non-monotonic approach and Finsler's technique. The benefit of the non-monotonic approach returns to the utility of an arbitrary number of quadratic functions by considering the higher order derivatives of the vector field in the CT case (or the higher order differences of the vector field in the DT case). Finsler's technique aims to solve the closed-loop stability problem in a larger parametric space. The strong points of the suggested LMI conditions are easy to program, eliminate the product between the state matrix and Lyapunov matrices, reduce the constraints by avoiding the decrease monotonically along trajectories for each quadratic Lyapunov function, guarantee the robust stability of the closed-loop by using a state-feedback gain. The simulation results show and confirm the effectiveness of these proposed conditions.

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基于非单调方法的不确定系统鲁棒状态反馈控制器

本文针对一维线性定常不确定系统的连续时间（CT）和离散时间（DT）两种情况，提出了新的线性矩阵不等式（LMI）条件，以保证系统闭环的鲁棒稳定性。这些改进的鲁棒状态反馈控制条件结合了非单调方法和Finsler技术。通过考虑CT情况下向量场的高阶导数（或DT情况下向量场的高阶差），非单调方法的好处又回到了任意数量的二次函数的效用。Finsler技术旨在解决更大参数空间中的闭环稳定性问题。所提LMI条件的优点是易于编程，消除了状态矩阵与Lyapunov矩阵之间的乘积，避免了每个二次Lyapunov函数沿轨迹单调递减，减少了约束，利用状态反馈增益保证了闭环的鲁棒稳定性。仿真结果验证了所提条件的有效性。

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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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.