Reinforcement learning algorithm for improving speed response of a five-phase permanent magnet synchronous motor based model predictive control
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View abstract on PubMed
Summary
This summary is machine-generated.A new reinforcement learning (RL) control algorithm enhances five-phase interior permanent magnet synchronous motor (5ph-IPMSM) performance. This RL-TD3 approach offers superior speed responses compared to metaheuristic optimization techniques.
Area Of Science
- Electrical Engineering
- Control Systems
- Artificial Intelligence
Background
- Five-phase interior permanent magnet synchronous motors (5ph-IPMSMs) are critical for advanced applications like electric vehicles.
- Optimizing control performance is essential for these systems.
Purpose Of The Study
- To develop and evaluate a novel reinforcement learning (RL) control algorithm for 5ph-IPMSM drives.
- To optimize motor speed response in both constant torque and constant power regions.
Main Methods
- A twin-delayed deep deterministic policy gradient (TD3) based RL algorithm was proposed.
- The RL algorithm tunes two cascaded PI controllers within a model predictive control (MPC) framework.
- Performance was benchmarked against Transit Search (TS), Honey Badger Algorithm (HBA), Dwarf Mongoose (DM), and Dandelion-Optimizer (DO).
Main Results
- The RL-TD3 algorithm demonstrated superior speed response compared to four metaheuristic optimization techniques.
- Key performance metrics including settling time, rise time, maximum time, and overshoot percentage were improved.
- The RL-TD3 achieved the minimum settling time among all tested methods.
Conclusions
- The proposed RL-TD3 control strategy significantly enhances 5ph-IPMSM drive performance.
- This method offers a more effective approach to optimizing motor speed control than current metaheuristic techniques.
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