Speed Control of Motor Based on Improved Glowworm Swarm Optimization

To better regulate the speed of brushless DC motors, an improved algorithm based on the original Glowworm Swarm Optimization is proposed. The proposed algorithm solves the problems of poor robustness, slow convergence, and low accuracy exhibited by traditional PID controllers. When selecting the glowworm neighborhood set, an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution. After the glowworm’s position is updated, the league selection operator is introduced to search for the global optimal solution. Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm. The mathematical model of the brushless DC motor is established, the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm, and the speed of the brushless DC motor is adjusted. In a Simulink environment, a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor, and this simulation was compared with a traditional PID control. The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.