Optimization of sensorless induction motor speed regulation system based on Quantum Genetic Algorithm

There are four PI controllers in induction motor vector control system when identifying motor speed using MRAS. Parameter tuning of PI controllers will influence system performance directly. To overcome the difficulty of simultaneous parameter tuning for the PI controllers, Quantum Genetic Algorithm (QGA) was used to optimize PI parameters in each loop. A fitness function is designed in this paper as the individual assessment index of Quantum Genetic Algorithm. The fitness function can evaluate system dynamic performance and restrict output amplitude of each PI controller. Simulation results based on MATLAB platform show that Quantum Genetic Algorithm has the advantages of rich population diversity and fast convergence speed, and it avoids the shortcomings of premature convergence and poor local convergence. And the dynamic performance of the induction motor vector control system is improved. by optimizing PI controller parameters using quantum genetic algorithm.