Sensorless ANN-Based Control for Permanent Magnet Synchronous Machine Drives

In this paper, a sensorless artificial neural network (ANN) speed control strategy of permanent magnet synchronous machines (PMSMs) is introduced as an alternative to conventional control techniques. The control strategy achieves accurate tracking by making use of ANN's learning capabilities to approximate the machine's nonlinear dynamics. On the other hand, an ANN-based observer is used to estimate rotor speed and the rotor position is obtained by direct integration to reduce the effect of the system's noise. Unlike other sensorless control strategies, no a priori offline training, weights initialization, voltage transducer or mechanical parameters knowledge is required. Furthermore, the stability of the overall closed-loop system is proved by Lyapunov stability theory. The controller is compared to the well-known vector control technique. Results for different situations highlight the higher performance of the proposed control approach in transient, steady-state, and standstill conditions.