Position control of permanent magnet synchronous motor speed sensorless servo system via backstepping

To improve the performance of permanent magnet synchronous motor (PMSM) speed sensorless drives, the radial basis function (RBF) neural network control and backstepping control are proposed to design the controllers, and the model reference adaptive system (MRAS) observer is also constructed in this paper. The precise position control of PMSM drive system is a more complicated problem due to its significant nonlinear coupling. To get better control of PMSM sensorless drives drive system, the RBF neural network controller is constructed as the position controller to get the reference speed. In addition, the reference currents and voltages are obtained by backstepping controller. Further, the MRAS observer is developed for identifying the rotor speed of PMSM based on the Popov stability criterion. The overall control system possesses global asymptotic stability according to Lyapunov stability theory. Simulation results clearly exhibit that the controllers guarantee the excellent tracking performance of the reference position signals.