Brushless DC motor Control System Based on RBF Neural Network

This paper proposes a modular two-degree-of-freedom spherical joint, which has a compact internal structure and high integration. Firstly, a six-degree-of-freedom manipulator is designed. Its core part is a two-degree-of-freedom spherical joint, and its internal structure is described in detail. Then, the mathematical modeling of the brushless DC(BLDC) motor in the joint is carried out, and the PID self-tuning control system of the BLDC motor based on the RBF neural network is designed. The control system uses the RBF neural network to identify the Jacobian information of the motor dynamic process, and adjusts the PID controller parameters in real time through Jacobian information and gradient descent algorithm, so as to optimize the performance of the entire control system. Finally, the RBF PID self-tuning control system is simulated. The simulation results show that the control system has excellent dynamic performance and steady-state performance.