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Observer‐based controller design for uncertain disturbed Takagi‐Sugeno fuzzy systems: A fuzzy wavelet neural network approach
International Journal of Adaptive Control and Signal Processing ( IF 3.1 ) Pub Date : 2020-11-02 , DOI: 10.1002/acs.3195
Zeinab Ebrahimi 1 , Mohammad Hassan Asemani 1 , Ali Akbar Safavi 1
Affiliation  

In this article, we develop a novel method to design a controller for nonlinear systems represented by Takagi‐Sugeno (T‐S) fuzzy model in the presence of unknown dynamics, uncertainties in parameters of nonlinear system and external disturbances. The control law is constituted two segments. The first segment derives from parallel distributed compensation (PDC) procedure, in which each control rule is drawn from the respective rule of T‐S fuzzy model. The second segment stems from fuzzy wavelet neural network (FWNN) estimator which is evoked by the hypothesis of multiresolution analysis (MRA) of wavelet transforms and fuzzy notions, so as to approximate the uncertainties and external disturbances in T‐S fuzzy model accurately. In this regard, the Lyapunov stability theorem is applied to acquire the adaptive learning laws for training and tuning online FWNN parameters such as the dilations, translations, and the weights of networks. Moreover, the asymptotic stability of the closed loop system is guaranteed based on the Lyapunov stability theorem. Furthermore, a development of the proposed method to observer‐based controller design for uncertain nonlinear systems descripted by T‐S fuzzy model is provided. The efficiency and robustness of the proposed method are illustrated by simulation outcomes. It is noteworthy that the proposed controller remarkably handles the uncertainties and external disturbances in T‐S fuzzy model without employing traditional conservative lemma and without considering bounds on uncertainties.

中文翻译:

不确定扰动的Takagi-Sugeno模糊系统的基于观测器的控制器设计:模糊小波神经网络方法

在本文中,我们开发了一种新颖的方法,可在存在未知动力学,非线性系统参数不确定性和外部干扰的情况下,设计以Takagi-Sugeno(TS)模糊模型为代表的非线性系统的控制器。控制律分为两个部分。第一部分来自并行分布补偿(PDC)程序,其中每个控制规则均来自于TS模糊模型的相应规则。第二部分来自模糊小波神经网络(FWNN)估计器,该估计器是由小波变换和模糊概念的多分辨率分析(MRA)假设引起的,以便准确地估计T-S模糊模型中的不确定性和外部干扰。在这方面,Lyapunov稳定性定理用于获取自适应训练定律,用于训练和调整在线FWNN参数,例如膨胀,翻译和网络权重。此外,基于李雅普诺夫稳定性定理可以保证闭环系统的渐近稳定性。此外,还提供了一种针对以TS模糊模型描述的不确定非线性系统的基于观测器的控制器设计方法的开发。仿真结果表明了该方法的有效性和鲁棒性。值得注意的是,在不采用传统保守引理和不考虑不确定性界限的情况下,所提出的控制器在处理T-S模糊模型中的不确定性和外部干扰方面表现出色。和网络的权重。此外,基于李雅普诺夫稳定性定理可以保证闭环系统的渐近稳定性。此外,还提供了一种针对以TS模糊模型描述的不确定非线性系统的基于观测器的控制器设计方法的开发。仿真结果表明了该方法的有效性和鲁棒性。值得注意的是,在不采用传统保守引理和不考虑不确定性界限的情况下,所提出的控制器在处理T-S模糊模型中的不确定性和外部干扰方面表现出色。和网络的权重。此外,基于李雅普诺夫稳定性定理可以保证闭环系统的渐近稳定性。此外,还提供了一种针对以TS模糊模型描述的不确定非线性系统的基于观测器的控制器设计方法的开发。仿真结果表明了该方法的有效性和鲁棒性。值得注意的是,在不采用传统保守引理和不考虑不确定性界限的情况下,所提出的控制器在处理T-S模糊模型中的不确定性和外部干扰方面表现出色。提供了一种基于TS模糊模型描述的不确定非线性系统基于观测器的控制器设计方法的开发。仿真结果表明了该方法的有效性和鲁棒性。值得注意的是,在不采用传统保守引理和不考虑不确定性界限的情况下,所提出的控制器在处理T-S模糊模型中的不确定性和外部干扰方面表现出色。提供了一种基于TS模糊模型描述的不确定非线性系统基于观测器的控制器设计方法的开发。仿真结果表明了该方法的有效性和鲁棒性。值得注意的是,在不采用传统保守引理和不考虑不确定性界限的情况下,所提出的控制器在处理T-S模糊模型中的不确定性和外部干扰方面表现出色。
更新日期:2020-11-02
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