Fuzzy C-means robust algorithm for nonlinear systems

作     者：Chen, Tim Kuo, D. Chen, C. Y. J. 

作者机构：Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City Vietnam Univ Calif Irvine Fac Informat Technol Irvine CA USA King Abdulaziz Univ Fac Ind Engn Jeddah 21589 Saudi Arabia 

出 版 物：《SOFT COMPUTING》 

年 卷 期：2021年第25卷第11期

页      面：7297-7305页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：LMI fuzzy criterion T– S fuzzy models Model-free sliding mode Fuzzy C-means clustering algorithm Fuzzy control Stability 

摘      要：This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy C-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. The control and stability problems in the present nonlinear systems are studied based on a Takagi-Sugeno (T-S) fuzzy model. A lately and important proposed integral inequality is considered and selected according to the method of the free weight matrix, with these comparatively flexible stability criteria which are determined in the numerical form of linear matrix inequalities (LMIs). Under the condition of the premise in which the controller and the control system partake the same rules, the method does not inquire the same number of membership functions and mathematical rules. In addition, the improved control is used for large-scale nonlinear systems, where the stability criterion of the closed T-S fuzzy system is obtained through LMI and rearranged through the membership function for machine learning . The close-loop controller criteria are derived by using the Lyapunov energy functions to guarantee the stability of the system . Eventually, an instance is presented to reveal the efficacy of evolution.