六相感应机的模型参考自适应预测电流控制

IF 7.4 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2025-11-01 Epub Date: 2025-04-23 DOI:10.1109/TIE.2025.3559971
Manuel R. Arahal;Manuel G. Satué;Federico Barrero;Juana Martínez-Heredia
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引用次数: 0

摘要

预测定子电流控制(PSCC)是一种灵活的技术,一直是与多相驱动相关的研究课题。代价函数所带来的灵活性成为代价函数调优的障碍。密集的试错测试通常在这种情况下进行。本文基于模型参考自适应控制的概念,提出了一种自适应控制方法。与以往的方法不同,该方法提供了负担很小的PSCC在线调谐。该建议的动机是对每个工作点使用最优加权因子(WFs)的想法。对六相感应电机进行了实例研究。自适应方法包括交叉项和动量,以处理导数相对于WF的不规则性。结果表明,适应性允许越过绩效指标的帕累托前沿。这在不损失最优性的情况下提供了灵活性。该建议是通过在实验室设置的实际实验来评估的。
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Model Reference Adaptive Predictive Current Control of Six-Phase Induction Machine
Predictive stator current control (PSCC) is a flexible technique that has been the subject of research in connection with multiphase drives. The flexibility that the cost function (CF) brings finds an obstacle in CF tuning. Intensive trial and error tests are usual in this context. In this article, an adaptive procedure is proposed based on the concept of model reference adaptive control. Unlike previous methods, the proposal provides on-line tuning of PSCC with very little burden. The proposal is motivated by the idea of using optimal weighting factors (WFs) for each operating point. A case study is developed for a six-phase induction machine. The adaptive method includes cross terms and momentum to cope with irregularities in the derivatives with respect to the WF. It is shown that adaptiveness allows crossing the Pareto front of performance indicators. This provides flexibility without optimality loss. The proposal is assessed with real experimentation on a laboratory set-up.
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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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