Fuzzy C-means robust algorithm for nonlinear systems

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.

Automated summary

This paper discusses the robust controller design for fuzzy C-means clustering algorithms that are resilient to plant disturbances and controller variations. The control and stability issues in nonlinear systems are studied using the Takagi-Sugeno fuzzy model, incorporating a new integral inequality and flexible stability criteria through linear matrix inequalities (LMIs). The method does not require the controller and control system to have the same number of membership functions and mathematical rules, demonstrating efficacy for large-scale nonlinear systems.