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 Karnik-Mendel (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.