Subtractive Fuzzy C-means Clustering Approach

To identify T-S models, this paper presents a so-called “subtractive fuzzy C-means clustering” approach, in which the results of subtractive clustering are applied to initialize clustering centers and the number of rules in order to perform adaptive clustering. This method not only regulates the division of fuzzy inference system input and output space and determines the relative member function parameters, but also overcomes the impacts of initial values on clustering performance. Additionally, the orthogonal least square algorithm is employed to identify the parameters of consequents and linearize the systems over every sample time, ultimately resulting in the entire T-S fuzzy models. With this approach available, a fuzzy model predictive control system is established, along with corresponding control algorithms derived, as well as control system simulations carried out which explicitly demonstrate the effectiveness of the proposed method.