Performance Enhancement of Wireless BLDC Motor Using Adaptive Reinforcement Learning for Sustainable Pumping Applications

This paper presents an adaptive reinforcement learning (RL)-based control strategy for a wireless power transfer (WPT)-fed brushless DC (BLDC) motor drive, aimed at enhancing efficiency in industrial applications. Conventional control methods for BLDC motors often result in higher energy consumption and increased torque ripple under dynamic load and voltage variations. To address this, an adaptive RL framework is implemented with pulse density modulation (PDM), enabling the controller to augment motor speed, torque, and input power in real time. The system is modeled and tested for a 48 V, 1 HP BLDC motor, powered through a 1.1 kW WPT system. Training is carried out across 10 learning episodes with varying load torque and speed demands, allowing the RL agent to adaptively minimize losses while maintaining performance. Results indicate a significant reduction in torque ripple to a minimum of 0.20 Nm, stable speed regulation within ±30 rpm, and improved power utilization compared to existing controllers. The integration of RL with WPT provides a robust, contactless, and energy-efficient solution that is suitable for sustainable industrial motor-pump applications.