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IEEE Roger Brockett Memorial Fund 罗杰·布罗克特纪念基金
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3310691
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引用次数: 0
Iury Bessa [PhDs in Control] 尤里·贝萨[控制博士]
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3291910
Iury Bessa
How would you pitch your Ph.D. dissertation in one paragraph?
你会如何用一段话来陈述你的博士论文?
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引用次数: 0
NCCR Automation in Switzerland [Institutes in Control] 瑞士的NCCR自动化[控制研究所]
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3291911
Elise Cahard, Florian Dšrfler, John Lygeros, Linda Seward
Can you provide a brief history of the events that led to NCCR Automation?
你能提供导致NCCR自动化的事件的简要历史吗?
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引用次数: 0
Chao Chen [PhDs in Control] Chao Chen[控制博士]
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3291899
Chao Chen
How would you pitch your Ph.D. dissertation in one paragraph?
你会如何用一段话来陈述你的博士论文?
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引用次数: 0
Control of Diesel Engines [25 Years Ago] 柴油发动机的控制[25年前]
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3291629
L. Guzzella, A. Amstutz
This article is intended to give control engineers an overview of models and controls of Diesel engines. The main emphasis is on the engine’s torque generation, including all necessary ancillary devices (turbocharger, injection-system, etc.) and pollutant emission. Other system components like power-train elements, vehicles, generators, etc., are not discussed. Other important topics that are not included are diagnosis, noise, vibrations and controller hardware. Moreover, the article focuses only on model-based controls.
这篇文章的目的是给控制工程师的模型和控制柴油发动机的概述。主要重点是发动机的扭矩产生,包括所有必要的辅助设备(涡轮增压器、喷射系统等)和污染物排放。其他系统组件,如动力传动元件,车辆,发电机等,不讨论。其他不包括的重要主题是诊断、噪声、振动和控制器硬件。此外,本文只关注基于模型的控件。
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引用次数: 0
IEEE App IEEE软件
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3310692
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引用次数: 0
Data-Driven Control Based on the Behavioral Approach: From Theory to Applications in Power Systems 基于行为方法的数据驱动控制:从理论到电力系统的应用
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3291638
Ivan Markovsky, Linbin Huang, Florian Dörfler
Physics aims to describe, classify, and predict natural phenomena, while engineering aims to design new or modify existing ones. A phenomenon is characterized by some observed variables.
物理学的目标是描述、分类和预测自然现象,而工程学的目标是设计新的或修改现有的现象。一种现象是由一些观察到的变量来表征的。
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引用次数: 15
Kernel Methods and Gaussian Processes for System Identification and Control: A Road Map on Regularized Kernel-Based Learning for Control 系统辨识与控制的核方法与高斯过程:正则化核学习控制的路线图
3区 计算机科学 Q2 Mathematics Pub Date : 2023-10-01 DOI: 10.1109/mcs.2023.3291625
Algo Carè, Ruggero Carli, Alberto Dalla Libera, Diego Romeres, Gianluigi Pillonetto
The commonly adopted route to control a dynamic system and make it follow the desired behavior consists of two steps. First, a model of the system is learned from input–output data, a task known as system identification in the engineering literature. Here, an important point is not only to derive a nominal model of the plant but also confidence bounds around it. The information coming from the first step is then exploited to design a controller that should guarantee a certain performance also under the uncertainty affecting the model. This classical way to control dynamic systems has recently been the subject of new intense research, thanks to an interesting cross-fertilization with the field of machine learning. New system identification and control techniques have been developed with links to function estimation and mathematical foundations in reproducing kernel Hilbert spaces (RKHSs) and Gaussian processes (GPs). This has become known as the Gaussian regression (kernel-based) approach to system identification and control . It is the purpose of this article to give an overview of this development (see “Summary”).
通常采用的控制动态系统并使其遵循期望行为的途径包括两个步骤。首先,从输入输出数据中学习系统的模型,在工程文献中称为系统识别的任务。在这里,重要的一点是不仅要推导出工厂的名义模型,而且要推导出它周围的置信限。然后利用来自第一步的信息来设计控制器,该控制器在不确定性影响模型的情况下也应保证一定的性能。由于与机器学习领域的有趣交叉,这种控制动态系统的经典方法最近成为了新的激烈研究的主题。新的系统识别和控制技术已经发展与函数估计和数学基础在核希尔伯特空间(RKHSs)和高斯过程(GPs)的再现。这被称为系统识别和控制的高斯回归(基于核的)方法。本文的目的是概述这一开发(请参阅“摘要”)。
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引用次数: 0
Prof. Roger Ware Brockett, 1938–2023 [Obituary] 罗杰·威尔·布罗基特教授,1938-2023年[讣告]
IF 5.7 3区 计算机科学 Q2 Mathematics Pub Date : 2023-08-01 DOI: 10.1109/mcs.2023.3273824
Louise Brewster Brockett
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引用次数: 0
Micro-Macro Control [About This Issue] 微观及宏观调控[关于此议题]
IF 5.7 3区 计算机科学 Q2 Mathematics Pub Date : 2023-08-01 DOI: 10.1109/mcs.2023.3273476
R. Sepulchre
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引用次数: 0
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