Design And Optimization Of Induction Motor Speed Sensorless Vector Control System Based On MRAS

In induction motor speed regulation system, speed sensor is used to measure motor speed. But the sensor will be affected by working environment easily, the system cost will be increased and the motor will be difficult to maintain. However, speed identification based on speed sensorless technique can overcome the defects mentioned above. Besides, PI parameters tuning and optimization to improve system performance is also very important. This paper researches deeply into speed identification based on speed sensorless technique and PI parameter optimization in induction motor vector control system. Model Reference Adaptive System(MRAS) based on improved counter electromotive force is adopted to identify motor speed. In order to achieve optimal control system, PI parameters(speed regulator、excitation current regulator、torque current regulator and the regulator of the speed identification module) of the control system are optimized by means of improved Quantum Immune Genetic Algorithm(QIGA).In Matlab simulation and hardware platform, verify the effectiveness of the work in this paper.The main works are as follows:Firstly, motor speed identification based on speed sensorless technique is researched and explained. MRAS based on improved counter electromotive force is adopted in this paper to identify motor speed. It has advantages of brief structure and easy realization. Speed identification can be realized just using stator voltage and stator current. The stability of speed identification subsystem is analyzed using small signal analysis method. Simulation research of induction motor speed sensorless vector control system is conducted on Matlab/Simulink, and the simulation results show that the identified speed can trace the motor speed effectively.Then, induction motor speed sensorless vector control system is optimized. Multi-performance index, including speed output tracking error of the system and regulator output constraint, is designed. And improved Quantum Immune Genetic Algorithm(QIGA)is applied to optimize PI parameters of speed regulator、excitation current regulator、torque current regulator and the regulator of the speed identification module simultaneously. Simulation researches about optimization of induction motor vector control system based on speed sensorless technique are conducted on Matlab/Simulink. Motor speed regulation system optimization effect is tested under Qiushi experimental platform. The experimental results show that the optimized system using improved Quantum Immune Genetic Algorithm(QIGA) has good performance.