Genetic Algorithm Optimization for High-Performance VSI-Fed Permanent Magnet Synchronous Motor Drives

Nowadays permanent magnet synchronous motor (PMSM) drives are widely used in many industrial applications. Since most of the PMSM drive systems with closed-loop vector control techniques are controlled with proportional plus integral (PI) controllers, there exists growing demands to obtain optimal PI gain parameters to achieve high-performance. To eliminate disadvantages of traditional PI optimization techniques, a novel PI controller optimization methodology based on the multi-objective genetic algorithm, NSGA-II (non-dominated sorting genetic algorithm II), is proposed in this paper to enhance PMSM drive system performances under various working conditions. With the optimal PI controller in a speed field oriented control scheme, the current controlled voltage-source-inverter-fed PMSM (VSI-fed PMSM) drive system shows outstanding dynamic and steady performances in simulation. Also, a practical PMSM drive system based on digital signal processor (DSP) is built and tested to verify the effectiveness of the multi-objective genetic algorithm optimization methodology for motor drive systems