Improving Speed Response of a Five-Phase Motor Using Reinforcement Learning
Author Information
Author(s): Hassan Ahmed M., Ababneh Jafar, Attar Hani, Shamseldin Tamer, Abdelbaset Ahmed, Metwally Mohamed Eladly
Primary Institution: Department of Electrical Power and Machines Engineering, Faculty of Engineering, Benha University, Shoubra, Cairo, Egypt
Hypothesis
Can a reinforcement learning algorithm optimize the speed response of a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system?
Conclusion
The proposed reinforcement learning algorithm significantly improves the speed response of the 5ph-IPMSM compared to traditional optimization techniques.
Supporting Evidence
- The RL-based TD3 algorithm resulted in the fastest settling time and lowest overshoot compared to other optimization techniques.
- Simulation results were obtained using MATLAB SIMULINK to validate the proposed control methodology.
- The study compared the RL approach with Transit Search, Honey Badger Algorithm, Dwarf Mongoose, and Dandelion-Optimizer.
Takeaway
This study shows that using a smart computer program can help a special motor work faster and better, making it useful for things like electric cars.
Methodology
The study used a reinforcement learning algorithm called TD3 to tune two PI controllers in a five-phase motor control system, comparing its performance with four recent metaheuristic optimization techniques.
Limitations
The study does not include experimental validation of the proposed methodology.
Digital Object Identifier (DOI)
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