Comparative study of metaheuristic optimised PI controllers for sensorless BLDC motor drives using nonlinear moving horizon estimation

Abstract Brushless DC (BLDC) motors are widely preferred due to their high reliability under heavy driving cycles. The factor that makes them reliable which is electrical commutation, can be affected by the hall sensor wearing out over time. To eliminate hall sensors, many sensorless control techniques emerged for speed control of the BLDC motor. This work presents a comparative study of metaheuristic-optimised Proportional-Integral (PI) controllers for a Nonlinear Moving Horizon Estimator (NMHE) based sensorless BLDC drive, integrated with a Hall sensor-assisted startup strategy to overcome the low-speed startup problem. The NMHE is used to estimate rotor position and speed from the measured electrical signals, and the estimated position is then used to generate virtual Hall signals for electronic commutation. To improve speed tracking performance, a comparison of conventional PI and metaheuristic-optimised PI controllers, namely Particle Swarm Optimisation (PSO)-PI, Grey Wolf Optimisation (GWO)-PI, and Dolphin Echolocation Algorithm (DEA ) -PI controllers are implemented under the same sensorless motor condition. The comparative performance analysis is validated using Root Mean Square Error ( RMSE) and Mean Absolute Error (MAE). RMSE of different algorithm approaches is given as 1.647, 0.9538, 0.6075, 0.639 km/hr, respectively. The results demonstrate that the GWO-PI tuning strategy gives the best speed tracking performance. Simulation results indicate that the proposed NMHE-based sensorless structure is stable throughout the drive cycle, while the optimum PI controllers give a better dynamic response than the conventional PI case. Therefore, an NMHE-based sensorless drive with metaheuristic-tuned PI control offers a robust, cost-effective, and high-accuracy solution for EV drive cycle applications.