Flux-Linkage Loop-Based Model Predictive Torque Control for Switched Reluctance Motor

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-09-04 DOI:10.1109/TIE.2024.3443955
Jun Cai;Xiaolan Dou;Shoujun Song;Adrian David Cheok;Ying Yan;Xin Zhang
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Abstract

To minimize the switch state combinations and lower the computational burden in traditional model predictive torque control (MPTC) strategies in switched reluctance motor (SRM), a flux-linkage loop-based MPTC is proposed in this article. In this method, the concept of direct torque control (DTC) of SRM is integrated with the MPTC, which adopts the voltage vector selection method of DTC and utilizes the flux-linkage loop of DTC to assist in selecting the candidate switch states in MPTC, thereby further reducing the number of candidate switch state combinations to two. In addition, an improved quadrature phase locked loop (PLL) scheme is introduced to replace the arctangent function to calculate the flux linkage sector angle, which makes the angle estimation robust. The proposed method was experimentally validated on a 12/8-pole SRM experimental platform and compared with current chopping control and the traditional MPTC methods. The experimental results demonstrate its superiority in reducing computational burden and lowering torque ripple.
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基于通量-联动回路的开关磁阻电机模型预测转矩控制
为了减少传统开关磁阻电机模型预测转矩控制策略中的开关状态组合,降低计算量,提出了一种基于磁链环的开关磁阻电机模型预测转矩控制策略。该方法将SRM的直接转矩控制(DTC)的概念与MPTC相结合,采用DTC的电压矢量选择方法,利用DTC的磁链环辅助MPTC中候选开关状态的选择,从而进一步将候选开关状态组合的数量减少到2个。此外,引入改进的正交锁相环(PLL)方案来代替arctan函数计算磁链扇形角,使角度估计具有鲁棒性。在12/8极SRM实验平台上对该方法进行了实验验证,并与斩波控制和传统MPTC方法进行了比较。实验结果证明了该方法在减少计算量和减小转矩脉动方面的优越性。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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