Design of an Improved Interval Type-2 Controller Using FCM and Supervised Clustering Algorithms

Abstract

: In the last few decades, the interval type-2 fuzzy controller has gained popularity in comparison with the type-1 fuzzy controller. This is due to the capability of the interval type-2 fuzzy controller to better handle uncertainty and imprecision. However, modeling an intervaltype-2 fuzzy controller brings about several hurdles. One of the challenges of the design of an interval type-2 fuzzy controller is the generation of the primary membership functions with the purpose of formulating the upper and lower membership functions. This paper proposes a technique by which two Gaussian primary membership functions are generated for an interval type-2 fuzzy set. The means for the Gaussian membership functions are generated by employing two clustering techniques, namely the standard fuzzy c-means clustering algorithm and a recently developed supervised clustering algorithm. The standard deviations of the Gaussian membership functions are optimally selected by using the differential evolution algorithm. Besides, the number of rules required for the proposed model is much smaller. The proposed controller is applied to an armature-controlled DC motor and the obtained simulation results are compared with those obtained for the conventional interval type-2 fuzzy controller. The robustness of the proposed controller is also checked by adding noise and an impulse disturbance during the simulation.