Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17936 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916541356834816 |
|---|---|
| author | Kazemikia, Danial |
| author_facet | Kazemikia, Danial |
| contents | Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state of RL in motor control, exploring various RL algorithms and applications. The review highlights RL's advantages, including model-free control, adaptability to changing conditions, and the ability to optimize for complex objectives. It also addresses challenges in applying RL to motor control, such as sim-to-real transfer, safety and stability concerns, scalability, and computational complexity. By providing a comprehensive overview of the field, this review aims to deepen understanding of RL's potential to revolutionize motor control and drive advancements across industries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Motor Control: A Comprehensive Review Kazemikia, Danial Systems and Control Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state of RL in motor control, exploring various RL algorithms and applications. The review highlights RL's advantages, including model-free control, adaptability to changing conditions, and the ability to optimize for complex objectives. It also addresses challenges in applying RL to motor control, such as sim-to-real transfer, safety and stability concerns, scalability, and computational complexity. By providing a comprehensive overview of the field, this review aims to deepen understanding of RL's potential to revolutionize motor control and drive advancements across industries. |
| title | Reinforcement Learning for Motor Control: A Comprehensive Review |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.17936 |