Abstract: Aiming at the problem that the traditional neural network proportionalintegraldifferential (PID) controller and linear quadratic regulator (LQR) optimized PID controller had long recovery time and poor antiinterference for speed control of brushless direct current motor， we proposed a BP neural network PID controller optimized by LQR for speed control of brushless direct current motor. Firstly, BP neural network was used to adjust the PID gain to improve the dynamic adaptability and robustness of the controller. Secondly, LQR was used to optimize the optimal output of BP neural network, which was closer to the target PID gain. The simulation results show that the controller can effectively improve the response speed, reduce the steadystate error and enhance the antiinterference ability.

Key words: brushless direct current motor, PID controller, BP neural network,  , linear quadratic regulator (LQR), speed control