  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.
