Application of Genetic Algorithms in Control of Induction Motor Drives

Abstract

In this study, mathematical models, as well as induction motor control methods were presented and this thesis specifically emphasized the vector control model for induction motors. A genetic algorithm is also mentioned in detail. The second part contains some applications of genetic algorithms controlling induction motors such as: PID Speed Controller Optimization Using Online Genetic Algorithm for Induction Motor Drive, Rotor Time Constant Estimation of Induction Motor Using online PI-Adaptive and GA-Adaptive Model, Estimation of IM Parameters Using ANN and GA, PI-based Speed Controller for Vector Control Model of the Induction Motor Drive Using GA Tuned Fuzzy algorithm, Parameters Estimation for Sensorless Control of Induction Motor Drive Using Modified GA and CSA Algorithm. Finally, the experimental results consist of the parts: Speed control system with FOC modelling for many different levels using encoder, online GA-PID controller based on genetic algorithm in FOC controller modelling of induction motor, parameters estimation of induction motor using genetic algorithm and ANN with experimental data. The thesis shows that the application of genetic algorithms in induction motor control has many positive effects.