Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291609
Magnus Egerstedt, Anuradha Annaswamy
The big, defining questions of our time, such as “How do we leave a viable environment for our grandchildren?”, “How do we get effective and affordable medical care to all corners of the planet?”, or “How do we use artificial intelligence and robotics to create societies that work for everyone?” require a multidisciplinary approach. However, control theory as a scientific discipline has its fingerprints all over those questions. As such, it is our responsibility to make sure that our tools and techniques transition out of our classrooms and conference venues and have a positive impact on our local and global communities. One example of this is the Empower a Billion Lives competition, where the IEEE Control Systems Society (CSS) partnered with the IEEE Power Electronics Society (PELS) for the second version of the competition through the usage of the newly established CSS Fund. This is a great example of multidisciplinary teamwork, the use of controls as a force for good, and our ambition to contribute to solutions that matter at a global scale.
我们这个时代的重大问题,比如“我们如何为我们的子孙留下一个可行的环境?”、“我们如何为地球上的每个角落提供有效和负担得起的医疗服务?”,或者“我们如何利用人工智能和机器人技术来创造适合每个人的社会?”“需要多学科的方法。然而,控制理论作为一门科学学科,在所有这些问题上都有自己的印记。因此,我们有责任确保我们的工具和技术走出我们的教室和会议场所,并对我们当地和全球社区产生积极影响。其中一个例子是Empower a Billion Lives比赛,IEEE控制系统协会(CSS)与IEEE电力电子协会(PELS)合作,通过使用新成立的CSS基金,举办了第二届比赛。这是多学科团队合作的一个很好的例子,利用控制作为一种向善的力量,以及我们为全球范围内重要的解决方案做出贡献的雄心。
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291634
Shun-Ichi Azuma
How did your education and early career lead to your initial and continuing interest in the control field?
你所受的教育和早期的工作是如何使你对控制领域产生最初和持续的兴趣的?
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291633
Lanlan Su
How did your education and early career lead to your initial and continuing interest in the control field?
你所受的教育和早期的工作是如何使你对控制领域产生最初和持续的兴趣的?
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291884
Caio Kalil Lauand, Sean Meyn
How can you optimize a function $Gamma{:}{mathfrak{R}}^{d}rightarrow{mathfrak{R}}$ based on evaluations of this function without access to its gradient? Kiefer and Wolfowitz proposed a solution in the early 1950s based on stochastic approximation (SA) [16] , and in the 1920s, an engineer for the French railway system proposed an entirely deterministic approach that is now known as extremum seeking control ( ESC ) [27] , [48] . Once you understand the ESC architecture, you will find that the ideas are very similar. A fundamental difference is that random noise is replaced with sinusoids for exploration.
{"title":"Quasi-Stochastic Approximation: Design Principles With Applications to Extremum Seeking Control","authors":"Caio Kalil Lauand, Sean Meyn","doi":"10.1109/mcs.2023.3291884","DOIUrl":"https://doi.org/10.1109/mcs.2023.3291884","url":null,"abstract":"How can you optimize a function <inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$Gamma{:}{mathfrak{R}}^{d}rightarrow{mathfrak{R}}$</tex-math></inline-formula> based on evaluations of this function without access to its gradient? Kiefer and Wolfowitz proposed a solution in the early 1950s based on stochastic approximation (SA) <xref ref-type=\"bibr\" rid=\"ref16\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">[16]</xref> , and in the 1920s, an engineer for the French railway system proposed an entirely deterministic approach that is now known as <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">extremum seeking control</i> ( <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">ESC</i> ) <xref ref-type=\"bibr\" rid=\"ref27\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">[27]</xref> , <xref ref-type=\"bibr\" rid=\"ref48\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">[48]</xref> . Once you understand the ESC architecture, you will find that the ideas are very similar. A fundamental difference is that random noise is replaced with sinusoids for exploration.","PeriodicalId":55028,"journal":{"name":"IEEE Control Systems Magazine","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135368085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291628
Rodolphe Sepulchre
This issue of IEEE Control Systems is the first of two special issues devoted to data-driven control. The guest editor for these two issues is Florian Dörfler, and he was helped by our associate editor, Daniel Quevedo. They assembled seven articles for this special issue, the first four of which appear in the present issue: “Data-Driven Control Based on the Behavioral Approach: From Theory to Applications in Power Systems,” by Ivan Markovsky, Linbin Huang, and Florian Dörfler [A1] ; “Kernel Methods and Gaussian Processes for System Identification and Control: A Road Map on Regularized Kernel-Based Learning for Control,” by Algo Carè, Ruggero Carli, Alberto Dalla Libera, Diego Romeres, and Gianluigi Pillonetto [A2] ; “Quasi-Stochastic Approximation: Design Principles With Applications to Extremum Seeking Control,” by Caio Kalil Lauand and Sean Meyn [A3] ; and “Data-Driven Safety Filters: Hamilton-Jacobi Reachability, Control Barrier Functions, and Predictive Methods for Uncertain Systems,” by Kim P. Wabersich, Andrew J. Taylor, Jason J. Choi, Koushil Sreenath, Claire J. Tomlin, Aaron D. Ames, and Melanie N. Zeilinger [A4] . The four articles are described in the introductory article by Guest Editor Florian Dörfler [A5] .
本期《IEEE控制系统》是两期关于数据驱动控制的特刊中的第一期。这两期的客座编辑是Florian Dörfler,他得到了我们的副编辑Daniel Quevedo的帮助。他们为本期特刊汇集了七篇文章,其中前四篇出现在本期:“基于行为方法的数据驱动控制:从理论到电力系统的应用”,作者:Ivan Markovsky, Linbin Huang和Florian Dörfler [A1];“系统识别和控制的核方法和高斯过程:基于正则化核的控制学习的路线图”,作者:Algo Carè, Ruggero Carli, Alberto Dalla Libera, Diego Romeres和Gianluigi Pillonetto [A2];Caio Kalil Lauand和Sean Meyn的《准随机逼近:设计原则与极值寻求控制的应用》[A3];和“数据驱动的安全过滤器:Hamilton-Jacobi可达性、控制障碍函数和不确定系统的预测方法”,作者:Kim P. wabersiich、Andrew J. Taylor、Jason J. Choi、Koushil Sreenath、Claire J. Tomlin、Aaron D. Ames和Melanie N. Zeilinger [A4]。客座编辑Florian Dörfler [A5]在介绍文章中介绍了这四篇文章。
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291929
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3310693
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291623
Christoforos N. Hadjicostis
How did your education and early career lead to your initial and continuing interest in the control field?
你所受的教育和早期的工作是如何使你对控制领域产生最初和持续的兴趣的?
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291914
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Pub Date : 2023-10-01DOI: 10.1109/mcs.2023.3291608
Rodolphe Sepulchre
Once upon a time, control systems were connecting sensors and actuators. Nowadays, they primarily process “data” like any other machine belonging to the modern age. Data are the new gold: the new source of wealth and power. Peter Sondergaard, global head of research at Gartner, said, “Information is the oil of the 21st century, and analytics is the combustion engine.” Eric Schmidt, executive chairman at Google, stated, “There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” It is all just data. More is better.
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