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Reinforcement Learning for Motor Control: A Comprehensive Review

Danial Kazemikia

TL;DR

This review investigates how reinforcement learning can address core challenges in electric-m motor control, including nonlinear dynamics, parameter uncertainties, and disturbances, by surveying model-free, data-driven approaches across PMSM, BLDCM, DC, and SRM drives. It categorizes algorithms into value-based, policy-based, and actor-critic methods, and examines both direct motor-control applications and optimization of conventional controllers, highlighting representative techniques such as DQN, DDPG, TD3, Q-learning, and IRL variants. Key contributions include synthesizing evidence on performance gains, robustness, and practical constraints like sim-to-real transfer, safety, and computational demands, while clarifying gaps in generalization and real-world deployment. The findings underscore RL's potential to enhance motor-control performance and adaptability, with significant implications for industrial automation, robotics, and aerospace, provided ongoing work addresses training efficiency, hardware requirements, and reliable deployment in real hardware.

Abstract

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.

Reinforcement Learning for Motor Control: A Comprehensive Review

TL;DR

This review investigates how reinforcement learning can address core challenges in electric-m motor control, including nonlinear dynamics, parameter uncertainties, and disturbances, by surveying model-free, data-driven approaches across PMSM, BLDCM, DC, and SRM drives. It categorizes algorithms into value-based, policy-based, and actor-critic methods, and examines both direct motor-control applications and optimization of conventional controllers, highlighting representative techniques such as DQN, DDPG, TD3, Q-learning, and IRL variants. Key contributions include synthesizing evidence on performance gains, robustness, and practical constraints like sim-to-real transfer, safety, and computational demands, while clarifying gaps in generalization and real-world deployment. The findings underscore RL's potential to enhance motor-control performance and adaptability, with significant implications for industrial automation, robotics, and aerospace, provided ongoing work addresses training efficiency, hardware requirements, and reliable deployment in real hardware.

Abstract

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.

Paper Structure

This paper contains 18 sections, 9 figures.

Table of Contents

  1. Introduction
  2. Fundamentals of Reinforcement Learning
  3. Motor-Specific Applications of RL for Motor Control
  4. Permanent Magnet Synchronous Motors (PMSMs)
  5. Brushless DC Motors (BLDCMs)
  6. DC Motors
  7. Switched Reluctance Motors (SRMs)
  8. Types of RL Algorithms in Electric Motor Control
  9. Value-Based Methods
  10. Policy-Based Methods
  11. Actor-Critic Methods
  12. Control Objectives in Motor Control using RL
  13. Direct Motor Control Applications
  14. Parameter Optimization
  15. Considerations and Limitations
  16. ...and 3 more sections

Figures (9)

  • Figure 2: A framework of RL learning process.
  • Figure 3: Speed profile of PMSM controlled by PID vs RL 19
  • Figure 4: Speed Error in PMSM controlled by PID vs RL 19
  • Figure 5: Block Diagram of the PMSM robust control based on proposed online off-policy IRL Algorithm 3
  • Figure 6: Simplified schematic of the overall control and drive system in 7
  • ...and 4 more figures