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Machine Learning for Control [About this Issue]

delete2024-08-01
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PRE
AI
R
Rodolphe Sepulchre
DOI：10.1109/MCS.2024.3402572delete
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Abstract

Abstract

En 中文
This IEEE Control Systems issue includes two feature articles and one focus on education. The first feature article [A1] is the transcript of the 2023 Bode Lecture by Miroslav Krstic. The author proposes a use of machine learning that leverages the control community's heritage of rigorous, certificate-bearing control designs. He employs, in partial differential equations (PDE) control, the recent breakthroughs in deep learning approximations of the so-called neural operators. With neural operators, entire PDE control methodologies are encoded into what amounts to a function evaluation, leading to a thousandfold speedup in real-time implementation, while retaining the stability guarantees. Applications range from traffic control and epidemiology to manufacturing, energy generation, and supply chains.

Journal

I
IEEE Control Systems Magazine
IF:
6.3
Papers:
1.8K
Citations:
4.7K

Organization

No organization information available