Neural network adaptive sliding mode control for permanent magnet synchronous motor

Considering the sensitivity to parameter variation and load disturbance of Permanent magnet synchronous motor (PMSM), this paper proposed a neural network based adaptive sliding mode control (NNASMC) for higher stability and robustness. RBF neural network was used to adjust the gain of the switch part of sliding mode control input. So the accurate mathematic model of the whole system including uncertain parameters and disturbance was not required. The stability of the system was proved by Lya-punov theory. Simulations and experiments are done under the situation of constant disturbance, time-varing disturbance and parameter variation. The proposed NNASMC has a better stability and noise reduction compared with PI control.