Reinforcement learning-driven model predictive control presents a significant advancement in the optimization of counter-rotating permanent magnet synchronous motors (PMSM) used in submarine propulsion systems. By integrating sophisticated artificial intelligence algorithms, this method enhances the overall efficiency and performance of these motors. The adaptation of reinforcement learning mechanisms allows for the real-time adjustment of operational parameters, resulting in improved energy consumption, torque management, and vibration reduction. As submarines demand highly reliable and efficient propulsion systems to ensure both stealth and operational effectiveness, this technology promises to deliver marked improvements over traditional control methods. The implementation of this innovative control strategy could lead to significant advancements in submarine propulsion, offering the potential for longer missions, reduced maintenance requirements, and enhanced operational capabilities.
