Disturbance rejections of polynomial fuzzy systems under equivalent-input-disturbance estimator approach

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
P. Selvaraj , O.M. Kwon , S.H. Lee , R. Sakthivel
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引用次数: 0

Abstract

This paper proposes an integrated robust stabilization and anti-disturbance control scheme for nonlinear systems using a polynomial fuzzy model approach. To estimate unknown disturbances, an equivalent-input-disturbance (EID) estimator is employed. The proposed approach incorporates a novel fuzzy model assisted by EID estimator into the sum-of-squares-based approach, utilizing a polynomial fuzzy model-based observer to estimate the disturbance effect. A suitable fuzzy rule-based control law is developed by utilizing the parallel distributed compensation approach and the output of the EID estimator. To ensure stability, fuzzy membership functions are converted into sum-of-square polynomials using a polynomial curve fitting approach, allowing their exact shape information to be used in the stability condition. The addressed system is transformed into an augmented system by incorporating the system, observer, and filter states, simplifying the analysis. Gain matrices for the controller and observer are obtained using Lyapunov stability theory and sum-of-squares methods to confirm asymptotic stabilization of the fuzzy system. Two numerical examples are presented to demonstrate the effectiveness of the proposed control design method.

等效输入扰动估计器方法下的多项式模糊系统扰动剔除
本文提出了一种采用多项式模糊模型方法的非线性系统鲁棒稳定和抗干扰综合控制方案。为了估计未知干扰,采用了等效输入干扰(EID)估计器。所提出的方法在基于平方和的方法中加入了由 EID 估算器辅助的新型模糊模型,利用基于多项式模糊模型的观测器来估算干扰效应。利用并行分布式补偿方法和 EID 估计器的输出,制定了合适的基于模糊规则的控制法则。为确保稳定性,使用多项式曲线拟合方法将模糊成员函数转换为平方和多项式,从而在稳定性条件中使用其确切的形状信息。通过纳入系统、观测器和滤波器状态,将所处理的系统转换为增强系统,从而简化了分析。利用 Lyapunov 稳定性理论和平方和方法获得了控制器和观测器的增益矩阵,从而确认了模糊系统的渐近稳定。本文列举了两个数值示例,以证明所提控制设计方法的有效性。
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来源期刊
Fuzzy Sets and Systems
Fuzzy Sets and Systems 数学-计算机:理论方法
CiteScore
6.50
自引率
17.90%
发文量
321
审稿时长
6.1 months
期刊介绍: Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.
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