Optimal design of T-S fuzzy controller for a nonlinear system using a new adaptive particle swarm optimization algorithm

Designing an optimal Takagi–Sugeno (T–S) fuzzy system for real–world non–linear control problems is a challenging problem. Complex non–linear system produces large fuzzy rule–based and requires large amount of memory. To overcome these problems, this paper proposes a hybrid approach to generate the optimal T–S fuzzy system. First, the Fuzzy Clustering Method (FCM) is employed to partitioning the input space and extracting initial fuzzy rule–based. Moreover, a new Adaptive Particle Swarm Optimization (APSO) technique is suggested to determine the optimal number of clusters in FCM, which is the same as the number of fuzzy rules. Finally, Recursive Least Square (RLS) method based on the Mean Square Errors (MSE) criterion is used to regulate the coefficients of the consequent part of initial fuzzy rules. Some simulations are conducted on a Non–Linear Inverted Pendulum (NLIP) system to support the efficiency of the proposed approach in designing compact and accurate T–S fuzzy systems.