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Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

pmid: 32733048
pmc: PMC7393368
AbstractThree-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.
- Universiti Tenaga Nasional Malaysia
- University of Technology Sydney Australia
- Universiti Tenaga Nasional Malaysia
- National University of Malaysia Malaysia
- University of Wollongong Australia
Artificial intelligence, Science, Genetic Fuzzy Systems, Electronic speed control, FOS: Mechanical engineering, Heuristic, Control (management), Science and Technology Studies, Article, Engineering, Interval Type-2 Fuzzy Logic, Analysis of Electric Machinery and Drive Systems, Artificial Intelligence, Fuzzy Rule-Based Systems, FOS: Electrical engineering, electronic engineering, information engineering, Control theory (sociology), Type-2 Fuzzy Logic Systems and Applications, Electrical and Electronic Engineering, Induction motor, Fuzzy Logic Systems, Biology, Transient (computer programming), Control engineering, Q, Controller (irrigation), Voltage, Stator, Computer science, Mechanical engineering, Agronomy, 629, Fuzzy logic, Algorithm, Operating system, Electrical engineering, Physical Sciences, Computer Science, Multilevel Converters in Power Electronics
Artificial intelligence, Science, Genetic Fuzzy Systems, Electronic speed control, FOS: Mechanical engineering, Heuristic, Control (management), Science and Technology Studies, Article, Engineering, Interval Type-2 Fuzzy Logic, Analysis of Electric Machinery and Drive Systems, Artificial Intelligence, Fuzzy Rule-Based Systems, FOS: Electrical engineering, electronic engineering, information engineering, Control theory (sociology), Type-2 Fuzzy Logic Systems and Applications, Electrical and Electronic Engineering, Induction motor, Fuzzy Logic Systems, Biology, Transient (computer programming), Control engineering, Q, Controller (irrigation), Voltage, Stator, Computer science, Mechanical engineering, Agronomy, 629, Fuzzy logic, Algorithm, Operating system, Electrical engineering, Physical Sciences, Computer Science, Multilevel Converters in Power Electronics
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