A shaft sensorless control for PMSM using direct neural network adaptive observer

Rotor position detection is necessary for phase commutation and current control in high-performance PMSM. The traditional detecting method is based on resolver, absolute encoder etc. This paper presents a position and velocity sensorless control algorithm based on a direct neural model reference adaptive observer. The proposed observer comprise two neural networks which are trained to learn the electrical and mechanical model respectively. Adaptation is realized by online training using current prediction error. Various advantages of this estimating scheme over other sensorless control schemes, such as robustness, nonlinear adaptation and learning ability is shown by extensive simulations.