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.

本文针对多种模糊C均值聚类算法的解决方案，提出了一种鲁棒控制器设计准则，该算法对工厂参数扰动和控制器增益变化具有鲁棒性。基于Takagi–Sugeno（TS）模糊模型，研究了当前非线性系统中的控制和稳定性问题。根据自由权重矩阵的方法，考虑并选择了一个最近且重要的积分不等式，并以线性矩阵不等式（LMI）的数字形式确定了这些相对灵活的稳定性标准。在控制器和控制系统具有相同规则的前提下，该方法不需要查询相同数量的隶属函数和数学规则。此外，改进的控制用于大型非线性系统，其中闭环TS模糊系统的稳定性准则通过LMI获得，并通过隶属函数进行重新排列，以进行机器学习。通过使用Lyapunov能量函数来推导闭环控制器准则，以确保系统的稳定性。最终，提出了一个实例来揭示进化的功效。