Optimized torque control of switched reluctance motor at all operational regimes using neural network
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abstract
Switched reluctance motor (SRM) optimal control parameters, which maximize torque per ampere, are calculated using a dynamic SRM model. In order to include the effect of the magnetic nonlinearity, static torque and flux-linkage data are used in the dynamic model. The static data are generated experimentally. To recreate these control parameters, online, artificial neural networks are used. Two separate networks are trained. One is trained with the low-speed control parameters for torque control at low speed, while the other is trained with the high-speed control parameters for torque control at high speed. The speed at which the SRM makes a transition from chopping control to single-pulse operation (i.e., low-speed to high-speed operation), commonly referred to as base speed, is torque (current) dependent. A small table is maintained in the controller to identify the base speed for different torque demands. When the motor exceeds the base speed for a certain torque demand, the controller switches from the low-speed neural network to the high-speed neural network and vice versa. It is also shown that the SRM is capable of producing an extended constant-horsepower operation with this optimal control. The power factor (the energy ratio) is shown to improve in this extended speed constant-horsepower range. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.
