Speed ripple minimization for interior-type PMSM using self-learning fuzzy control strategy

Permanent-magnet synchronous motor (PMSM) drives are widely used for high-performance industrial applications where torque smoothness is an essential requirement. However, the parasitic torque pulsations deteriorates the drive performance particularly at low-speeds. To suppress these speed ripples, a parameter self-learning hybrid fuzzy controller was implemented with the objective of minimizing speed ripples originated by torque pulsations. A three-term fuzzy controller is implemented by simply using a two-term fuzzy control rule-base without any increase of rules. The method of fuzzy inference based on phase plane had less computational burden, while the fuzzy inputs could be continuous. The control parameters are self-tuned by introducing a single neuron together with a back-propagation learning algorithm. This method has simpler structure and control algorithms and can be realized online easily. The simulation results and experiment results of 20 kW PMSM in electric car are given, the experiment results show that the parameter self-learning hybrid fuzzy vector control system can minimize the speed ripple efficiently.