Rotor resistance and excitation inductance estimation of an induction motor using deep-Q-learning algorithm

Abstract

To estimate the parameters of an induction motor in a data-based manner, this paper proposes a new offline method to estimate rotor resistance and excitation inductance based on the deep-Q-learning approach. In this method, parameter estimation can be facilitated without disturbing the model error or operating state. To achieve this goal, three important elements, namely observation, action, and reward, are appropriately designed. To improve the robustness and accelerate the convergence, a new concept, denoted as Q-sensitivity, is proposed and investigated in detail. The experimental results show that a high-Q-sensitivity design can allow the proposed method to obtain a fast and torque-maximized estimation. Results from the comparative studies confirm the accuracy and robustness of the proposed method.