Simplified interval type-2 fuzzy logic system based on new type-reduction

Interval type-2 fuzzy logic systems (IT2-FLSs) have recently been utilized in many control processes due to their ability to model uncertainties better than their type-1 (T1) counterparts. While an IT2-FLS has the capability to model more complex relationships, the output of a type-2 fuzzy inference engine needs to be type-reduced. The high computational cost of the type-reduction (TR) process means that it is more expensive to deploy the IT2-FLSs, which may hinder them from certain cost-sensitive real-world applications. This paper proposes a new method of TR to reduce their computational cost and also, it improves the performance of the IT2-FLS. The proposed TR method is compared with the KarnikMendel (KM) based TR method which is the standard way to do these operations and other alternative TR methods. The simulation results show that the performance of the IT2-FLS based on the proposed TR method is made significantly improved the performance over a wide range of structured uncertainties and the effect of the external disturbances.