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Vector Control as a Data Compressor
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AI
DOI：10.1109/tie.2026.3730116.png)
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Abstract
En 中文
Vector control is one of the most successful methods in AC motor drives. Despite being used for many years, vector control still remains dominant in industrial applications. This naturally raised interest to explore the fundamental reasons behind its success. In this article, we investigate vector control from an information-theoretic perspective, showing that vector control can be regarded as a data compressor to minimize the information contained in input data. Then, some in-depth analysis is conducted, revealing that under the standard balanced-fundamental assumption, the output of vector control approaches the rate distortion bound of the input signal. Moreover, compared with other motor control methods, vector control achieves the highest compression rate with the least amount of information. In experimental sections, two independent experiments are designed to verify the consistency between vector control and data compression. Besides, the experimental results also reveal a new insight that extends the core idea of vector control to a broader range of scientific domains: when exploring a system whose model is unknown but operational data is available, it is possible to extract certain model knowledge through extreme compression of the operational data.
Keywords:
Data compression
information theory
vector control
Journal
IF:
7.2
Papers:
1.8W
Citations:
9.8W
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