Q-Learning Scheduling for Tracking Current Control of Switched Reluctance Motor Drives


This paper presents a novel technique for controlling the current of Switched Reluctance Motor (SRM) drives based on reinforcement learning. The proposed current controller is based on a new scheduled Q-learning. Solving the infinite horizon linear quadratic tracker (LQT) problem for an unknown dynamic system of SRM drive, a new control scheme relying on the Q-learning algorithm is introduced for that purpose. The reference current generator of the SRM drive has been incorporated into the augmented system. A Q-learning algorithm is implemented to obtain the optimum solution of Algebraic Riccati Equation (ARE) with the absence of any data about system dynamics of SRM or the reference current generator. Additionally, a scheduling mechanism switches between Q matrices to allow for a nonlinear control using a table of Q-learning cores. After the introduction of the control scheme, a simulation has been designed to evaluate the performance of the proposed controller.

Meeting Name

2020 IEEE Power and Energy Conference at Illinois, PECI 2020 (2020: Feb. 27-28, Champaign, IL)


Electrical and Computer Engineering

Research Center/Lab(s)

Center for Research in Energy and Environment (CREE)

Keywords and Phrases

Controllers; Digital storage; Electric current control; Electric drives; Electric machine control; Learning algorithms; Learning systems; Reinforcement learning; Riccati equations; Scheduling, Algebraic Riccati equations; Current controller; Linear quadratic trackers; Non linear control; Q-learning algorithms; Reference current generators; Scheduling mechanism; Switched reluctance motor drives, Reluctance motors

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Feb 2020