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.

中文翻译：

---

用于控制的机器学习 [关于此问题]


本期 IEEE 控制系统包括两篇专题文章和一篇关于教育的文章。第一篇专题文章 [A1] 是 Miroslav Krstic 在 2023 年 Bode 讲座上的文字记录。作者提出了一种机器学习的使用方法，它利用了控制社区严格的、带有证书的控制设计的传统。他在偏微分方程 （PDE） 控制中采用了所谓的神经算子的深度学习近似的最新突破。借助神经运算符，整个 PDE 控制方法被编码为相当于函数评估的内容，从而将实时实现的速度提高了一千倍，同时保留了稳定性保证。应用范围从交通控制和流行病学到制造、能源生产和供应链。