BP Neural Network-intelligent PID Synovial Observation Vector Control Algorithm for Permanent Magnet Synchronous Motor
In order to solve the problems of permanent magnet synchronous motor(PMSM)such as large over-shoot and low detection precision of rotor position,BP neural network-intelligent PID sliding-mode observer control method was proposed in this paper.BP neural network was combined with traditional PID control and BP neural network was used to regulate PID gain online and control PMSM stably at the time of start and impact load interference.The sliding-mode observer structure was built in the coordinate system of PMSM mathemati-cal model by means of position-sensorless control,and the simulation model was built in MATLAB/Simulink simulation system for simulation analysis;finally,the effectiveness of BP neural network-intelligent PID control was assessed and verified by simulation from aspects of PID parameter,motor speed,etc.Through simulation analysis,the error between the actual position and the expected position of rotor detected with sliding-mode ob-server was less than 7％,and the actual position exactly coincided with the expected position after 0.3 seconds.The overshoot of PMSM using BP neural network-intelligent PID control reduced by 10.6％at the time of start and reduced by 1.4％at the time of impact load interference.The results showed that compared with traditional PID control,BP neural network-intelligent PID control could greatly improve the self-adaptability and the capac-ity of resisting interference of PMSM and significantly reduced the overshoot of PMSM at the time of start and impact load.
PMSMBP neutral networkintelligent PIDsliding-mode observersensorless control
万方数据
