New Sliding Mode Control Based on Tracking Differentiator and RBF Neural Network

In order to solve the problem that control system of permanent magnet synchronous motor (PMSM) is difficult meet high accuracy due influence non-repeated disturbances such as external disturbance, parameter variation, and friction force during operation, a novel sliding mode (NSMC) method based on tracking differentiator (TD) radial basis (RBF) neural network was proposed. Firstly, new reaching law proposed by adding state variables traditional exponential law, which can effectively reduce chattering system. Then, speediest designed estimate given speed signal its differential signal, realize variable structure algorithm. Finally, RBF used compensate for uncertainty interference system; robustness further improved adaptive weight updating. The simulation results show that, comparing with approach overshoot 22 r/min reduced hybrid decrease amplitude 77.1% under load recovery time shortened 0.059 s. After optimization network, 86 reduced, disturbance again 48.5%, 0.073