A comparative experimental study of direct torque control based on adaptive fuzzy logic controller and particle swarm optimization algorithms of a permanent magnet synchronous motor

Direct torque control (DTC) of permanent magnet synchronous motor (PMSM) drives is receiving increasing attention due to important advantages, such as fast dynamic and low dependence on motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. In this paper, two intelligent approaches are proposed in order to improve DTC performance. The first approach is based on two adaptive fuzzy logic controllers (AFLC). The first AFLC replaces the conventional comparators and switching table and the second AFLC adjusts in real time the outer loop PI parameters. In the second approach, particle swarm optimization (PSO) is used as another alternative to adjust the PI parameters. Simulation and experimental results demonstrate the effectiveness of the proposed intelligent techniques. Besides, the system associated with these techniques can effectively reduce flux and torque ripples with better dynamic and steady state performance. Quantitatively, PSO-based DTC approach reduces greatly flux and torque ripples. Further, PSO-based approach maintains a constant switching frequency which improves the PMSM drive system control performance.