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| Torque ripple suppression for switched reluctance motors based on current self-adaptive control |
| DANG Xuan-ju 1 MIAO Mao-yu 1 JIANG Hui 1 WU Xi-ru 1 LI Shan 2 |
1.College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China；
2. Division of Teaching Practice, Guilin University of Electronic Technology, Guilin 541004, China |
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Abstract A switched reluctance motor (SRM) has strong nonlinear characteristics due to its double-salient structure, nonlinear magnetic circuit and pulse power supply mode. In traditional control, the SRM linear torque model is used to calculate reference current to lead to large torque ripple when operating. Here, the SRM control strategy combining the self-adaptive PID control based on torque deviation’s double-weight neural network (DWNN) and the feed-forward compensation control based on finite-difference extended Kalman filtering (FDEKF) to predict current was proposed. The pretreatment of deviation was used to nonlinearly process torque deviation to realize the control with "small error, large gain, large error, small gain". Then, the self-adaptive PID control based on DWNN was used to control current. The current prediction was adopted to form the reference current’s feed-forward compensation control to improve the one-step predictive ability of the control system. The current was predicted based on FDEKF, the difference between the predicted current and the reference one was used to compensate the reference current in real time. After optimization, the effective controlled current was obtained under constant torque to realize indirectly the effective control of the total torque. Simulation results showed that the proposed control strategy can effectively suppress torque ripple of SRMs.
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Received: 14 June 2016
Published: 28 January 2018
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