Motor Speed Control Based on RBFNN Optimized Interference Observation
In order to reduce the effects of random fluctuations or sudden changes of load torque on the speed control performance of permanent magnet synchronous motor effectively and improve the anti-disturbance performance,a speed control method based on the disturbance observer optimized by radial basis function neural networks(RBFNN)is proposed in this paper.Firstly,considering the load torque as the interference of complex work conditions to the system,a disturbance observer based on the inverse model is designed to observe the load torque.Secondly,considering the low efficiency of manual parameter tuning,the poor adaptability and anti-disturbance performance of the fixed time constant,the RBFNN is used to automatically optimize the filter time constant to achieve accurate and fast observation.Finally,the observed value is used as a feedforward compensation term to suppress the influence of load disturbance.The simulation results indicate that the load torque is observed accurately and quickly,and the efficiency of parameter tuning is improved through the proposed method.Meanwhile,the simulation results demonstrate that the proposed method is with better anti-disturbance performance of faster observation,smaller speed fluctuation and faster speed response than the manual parameter tuning.
万方数据
维普
