Decentralized fuzzy C-means robust algorithm for continuous systems

Abstract

Purpose This paper aims to address the robust controller design problem for a class of fuzzy C-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. Based on Takagi–Sugeno (T-S) fuzzy model description, the stability and control problems of nonlinear systems are studied. Design/methodology/approach A recently proposed integral inequality is selected based on the free-weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs). Findings Under the premise that the controller and the system share the same, the method does not require the number of membership functions and rules. Practical implications Furthermore, the modified controller in a large-scale nonlinear system is utilized as a stability criterion for a closed-loop T-S fuzzy system obtained by LMI, and is rearranged by a machine learning membership function. Originality/value The closed-loop controller criterion is derived by energy functions to guarantee the stability of systems. Finally, an example is given to demonstrate the results.