Parameter optimization in sensorless speed control of permanent magnet synchronous motor

Permanent magnet synchronous motors (PMSM) are widely used in drive systems and industrial applications requiring high performance with their high efficiency, power density, small size and fast dynamic response characteristics. In applications requiring high precision, accurate speed information is required for permanent magnet synchronous motors to operate with high efficiency. Although sensors provide this information, sensorless control methods have gained importance in recent years due to problems such as cost, size, weight increase and decreasing system reliability. In this thesis, the speed control of a permanent magnet synchronous motor without using a speed sensor is implemented. In order to achieve the best speed control performance of the permanent magnet synchronous motor, various algorithms have been used in the optimization of the PI parameters of the speed estimator model reference adaptive system (MRAS) technique applied for sensorless control. These are Genetic Algorithm (GA), Jellyfish Search Algorithm (JSA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, Arithmetic Optimization Algorithm (AOA), Artificial Rabbit Optimization (ARO) algorithm, Weighted Average of Vectors (INFO) algorithm, Symbiotic Organisms Search (SOS) algorithm, Artificial Bee Colony (ABC) algorithm, Fire Hawk Optimization (FHO) algorithm and Runge-Kutta (RK) algorithm approaches are used. Sensorless field- oriented vector control of a permanent magnet synchronous motor is implemented in Matlab/Simulink environment. In the study, the performances of the specified optimisation algorithms are evaluated under constant speed, varying speed and sudden load change of the motor.