Interval Type-2 Fuzzy Neural System Based Control with Recursive Fuzzy C-Means Clustering

This paper focuses on the design of a novel control approach. Its contribution to the existing literature is that in the design of an interval type-2 fuzzy neural system, recursive fuzzy c-means clustering algorithm is used and the designed algorithm is applied in control applications. The center and the standard deviation values of the interval type-2 Gaussian membership functions at the antecedent part of the Takagi-Sugeno-Kang type fuzzy rules are determined by the use of the recursive fuzzy c-means clustering algorithm. The parameters at the consequent parts are tuned based on the gradient descent approach. The effectiveness of the designed algorithm is tested by simulation studies on a 2-DOF helicopter system and by experimental studies on a real-time servo system. The performance of the proposed method is compared with a traditional neuro-fuzzy structure and an interval type-2 fuzzy neural system, which are both adopted from the literature. The results obtained illustrate the efficacy of the proposed control approach.