Q-learning optimized diagonal recurrent neural network control strategy for brushless direct current motors

Abstract

In order to improve the working stability of brushless direct current motors (BLDCM), a diagonal recursive neural network (DRNN) control strategy based on Q-learning algorithm is proposed in this paper which is called as Q-DRNN. In Q-DRNN, DRNN iterates over the output variables through a unique recursive loop in the hidden layer, and its key weight is optimized to speed up the iteration. Moreover, an improved Q-learning algorithm is introduced to modify the weight momentum factor of DRNN, which makes DRNN have the ability of learning and online correction so as to make the BLDCM achieve better control effect. In MATLAB/Simulink environment, Q-DRNN is tested and compared with other popular control methods in terms of speed and torque response under different operating conditions, and the results show that Q-DRNN has better adaptive and anti-interference ability as well as stronger robustness.