An Improved Model-Predictive Torque Control of Switched Reluctance Motor Based on Sector Adaptive Allocation Technology
Ping Ren, Jingwei Zhu, Yonghan Liu, Yan Zhao, Zhe Jing
Abstract
In view of the disadvantages of negative torque and large torque ripple under different operating conditions in the traditional model predictive torque control (MPTC), an improved model predictive torque control of switched reluctance motor based on sector adaptive allocation technique is proposed in this paper. Firstly, considering the problem of negative torque under high speed and heavy load, a novel sector allocation principle is proposed to effectively limit the torque ripple and negative torque. The pre-selected voltage vector is re-designed and its number is minimized, which effectively reduces the computation of MPTC. Secondly, the calculation method for optimal angle is proposed to change the position and width of the sector, the width of the sector is calculated by the method of grasping the angle of the special position, and the position of the sector is determined by the calculation function of the turn-on and turn-off angles. By combining the optimum angle calculation method with sector allocation, the sector adaptive allocation technique is realised, which enables the proposed algorithm to adaptively adjust under various operating conditions. Finally, simulation and experimental results have verified that the proposed method can be applied to various operating conditions with lower torque ripple and higher torque ampere ratios compared to some other improved MPTC methods.