S.-C. Wang, Y.-L. Chen, Y.-H. Liu, Y.-C. Chen, and S.-Z. Lin (Taiwan)
Adaptive iterative learning, energy conversion, torque ripple, switched reluctance motor.
In this paper, an iterative learning control based on the accurate magnetization characteristics of the SRM is proposed to minimize the torque ripple and excitation energy conversion losses by adaptively tuning the energization parameters of commutation angles and duty ratio. The electromagnetic energy conversion and torque in SRM are functions of the flux-linkage, current, and rotor angle. The optimal excitation current profile will result in optimal speed response, co-energy generation, and minimum torque ripple. An automatic characterizing system is developed to accurately identify the SRMs’ static magnetization curves and take the nonlinearity of the magnetic circuit into account. The dSPACE DS1104 controller is used to setup drive system for simulation and implementation. Experimental tests on a 4-phase 8/6 pole SRM at different operation conditions are given to demonstrate the effectiveness and performance of the proposed method.
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