Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms

Abstract

A Type-2 Fuzzy logic controller adapted with quantum genetic algorithm, referred to as type-2 quantum fuzzy logic controller (T2QFLC), is presented in this article for robot manipulators with unstructured dynamical uncertainties. Quantum genetic algorithm is employed to tune type-2 fuzzy sets and rule sets simultaneously for effective design of interval type-2 FLCs. Traditional fuzzy logic controllers (FLCs), often termed as type-1 FLCs using type-1 fuzzy sets, have difficulty in modeling and minimizing the effect of uncertainties present in many real time applications. Therefore, manually designed type-2 FLCs have been utilized in many control process due to their ability to model uncertainty and it relies on heuristic knowledge of experienced operators. The type-2 FLC can be considered as a collection of different embedded type-1 FLCs. However, manually designing the rule set and interval type-2 fuzzy set for an interval type-2 FLC to give a good response is a difficult task. The purpose of our study is to make the design process automatic. The type-2 FLCs exhibit better performance for compensating the large amount of uncertainties with severe nonlinearities. Furthermore, the adaptive type-2 FLC is validated through a set of numerical experiments and compared with QGA evolved type-1 FLCs, traditional and neural type-1 FLCs.