Speed Change Response of Switched Reluctance Motor Drives under a Scheduled Q-Learning Scheme

Abstract

This paper investigated the speed change response of the scheduled Q-learning adaptive control of Switched Reluctance Motor (SRM) drives. This novel algorithm includes a scheduling approach to permit controlling the nonlinear domain of an SRM using a set of Q-learning cores, each of which is a Q-learning controller at a local linear operating point, which expands over the nonlinear surface of the system. Despite the effective tracking performance of this algorithm, the main issue with the use of this controller for SRM application is that motor speed appears inside the model of the machine and hence the Q-cores are directly impacted by the speed. To cope with this issue, the Q-table should retrain the Q-matrices whenever the rotational speed changes. This causes a slow speed change response due to learning process. In this paper, a new 3D Simulation and experimental results have illustrated the speed change response of SRM at different stages of the operation condition.

Meeting Name

52nd North American Power Symposium, NAPS 2021 (2021: Apr. 11-13, Tempe, AZ)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Adaptive Dynamic Programming (ADP); Current Control; Least Square (LS); Linear Quadratic Tracker (LQT); Reinforcement Learning (RL); Switched Reluctance Motor (SRM)

International Standard Book Number (ISBN)

978-172818192-9

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

13 Apr 2021

Link to Full Text

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