Interval Type-2 Fuzzy Logic Controller (IT2FLC) where the fuzzy parameters, e.g. Fuzzy Membership Functions and Fuzzy Rule Bases are tuned by Genetic Algorithm (GAs) known as Genetic Interval Type-2 Fuzzy System (GIT2FS). The GFS are discussed briefly, later, the proposed design is explained. Key wordsGA, Interval Type-2 Fuzzy Logic Controller (IT2FLC), Genetic Interval Type2 Fuzzy System (GIT2...

in this study, a novel variable impedance control for a lower-limb rehabilitation robotic system using voltage control strategy is presented. the majority of existing control approaches are based on control torque strategy, which require the knowledge of robot dynamics as well as dynamic of patients. this requires the controller to overcome complex problems such as uncertainties and nonlinearit...

Energy is a critical factor to obtain a sustainable development for countries and governments. Selection of the most appropriate energy alternative is a completely critical and a complex decision making problem. In this paper, an integrated multi-criteria decision-making (MCDM) methodology based on type-2 fuzzy sets is proposed for selection among energy alternatives. Then a roadmap has been cr...

The operation planning problem encounters several uncertainties in terms of the power system’s parameters such as load, operating reserve and wind power generation. The modeling of those uncertainties is an important issue in power system operation. The system operators can implement different approaches to manage these uncertainties such as stochastic and fuzzy methods. In this paper, new ...

Two advanced controllers were developed in this paper, i.e., a type-1 and a type-2 fuzzy logic controller and their performances compared in simulation for a case study, i.e., a binary distillation column, which is characterized by high nonlinearities and parameter uncertainties in the underlying mathematical model. Priority was given to the type-2 fuzzy controller, with special attention to in...

Abstract. Recently, a number of extensions to classical fuzzy logic systems (type-1 fuzzy logic systems) have been attracting interest. One of the most widely used extensions is the interval type-2 fuzzy logic systems. An interval type-2 TSK fuzzy logic system can be obtained by considering the membership functions of its existed type-1 counterpart as primary membership functions and assigning ...

For general type-2 fuzzy sets, the defuzzification process is very complex, and the exhaustive direct method of implementing type-reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type-2 fuzzy inferencing systems in real world applications. The present situation will not be expected to change, unless an efficient and fast met...

Fuzzy Logic Systems are widely recognized to be successful at modelling uncertainty in a large variety of applications. While recently interval type-2 fuzzy logic has been credited for the ability to better deal with large amounts of uncertainty, general type2 fuzzy logic has been a steadily growing research area. All fuzzy logic systems require the accurate specification of the membership func...