# Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-learning Scheduling

arXiv: https://arxiv.org/abs/2006.07764 · HTML: https://mlsanity.com/paper/2006.07764/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends](https://mlsanity.com/paper/2110.05403/) (2021)
- [Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control](https://mlsanity.com/paper/2201.13331/) (2022)

## Abstract (arXiv)

In this paper, a novel Q-learning scheduling method for the current controller of switched reluctance motor (SRM) drive is investigated. Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. This paper will introduce a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of a SRM model without involving any information about the model parameters to track the reference current trajectory by scheduling infinite horizon linear quadratic trackers (LQT) handled by Q-learning algorithms. Additionally, a linear interpolation algorithm is proposed to guide the transition of the LQT between trained Q-cores to ensure a smooth response as state variables evolve on the nonlinear surface of the model. Lastly, simulation and experimental results are provided to validate the effectiveness of the proposed control scheme.