Design of a Speed Adaptive Controller for a PMSM using Artificial Intelligence

Permanent magnet synchronous motors have been widely used in variable speed drives; however, the control scheme must ensure high requirements of dynamic performance. In this work, a comparative analysis of a synchronous motor response with four control strategies-conventional proportional integral, sliding mode, fuzzy logic, and neural networks-is exposed. The motor model and the current controller are described; this allows the control laws design. In addition, a nonlinear observer for estimating the rotor speed and load torque is designed. The performance of each driver is analyzed using time simulations where the motor is subjected to disturbances and reference changes. The proposed control technique using neural networks exhibits the best performance because it can adapt to every condition, demanding low computational effort for an online operation and considering the system nonlinearities.