EAGER: Extensible Linear Distributed Controllers for Large Multi-Input-Multi-Output Systems, with Applications to Control of Networked Printers

EAGER：适用于大型多输入多输出系统的可扩展线性分布式控制器，可用于控制网络打印机

• 批准号: 1050005
• 负责人: Cedric Langbort
• 金额: $ 15万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1050005&HistoricalAwards=false
• 关键词: EAGER; Extensible; Linear; Distributed; Controllers

The objective of this EArly-concept Grants for Exploratory Research (EAGER)research project is the control design for complex multi-input multi-output systems with very large state-space dimension, partially unknown state-space models, and in which the unknown part of the model, although identifiable in practice, requires a time-consuming or costly process to be estimated. This class of plants covers a large number of industrial and cyberphysical systems, from chemical processes to manufacturing systems to high-quality multi- machine xerographic reproduction systems, which constitute the main driving application and testbed for our work. For such systems in which precise model identification is possible but costly, the control engineer typically faces a trade-off between identification and closed-loop performance. Finding the appropriate ``critical parameters? to be identified for a particular regulation task and determining a ``good" identification/performance trade-off has traditionally been mostly a matter of experience and rules of thumb, and often occupies a significant portion of the practicing control engineer's time. This project proposes a new approach for developing practical techniques that can help the practitioner explore the identification/control performance trade-off. The key idea is to build on novel and fast ``compressed sensing" algorithms from the field of signal processing, and combine them with more traditional control design ideas. This research will be conducted in close collaboration with scientists at Xerox Corporation?s Research Center in Webster (NY, which will ensure the relevance and practical applicability of the developed algorithms, and provide access to an actual xerographic printing testbed. This collaboration will also actively engage a graduate student. We expect the tools resulting from this research to be widely applicable in industrial control design contexts. Their dissemination will be facilitated by the organization of tutorial workshops at leading control conferences, and the initial development of an open-source software toolbox.

这个早期概念的探索性研究赠款（EAGER）研究项目的目标是复杂的多输入多输出系统的控制设计非常大的状态空间维数，部分未知的状态空间模型，其中模型的未知部分，虽然在实践中可识别的，需要一个耗时或昂贵的过程来估计。这类工厂涵盖了大量的工业和网络物理系统，从化学工艺到制造系统，再到高质量的多机静电复印系统，这些系统构成了我们工作的主要驱动应用和测试平台。对于这样的系统，其中精确的模型识别是可能的，但昂贵的，控制工程师通常面临着识别和闭环性能之间的权衡。找到合适的“关键参数”？为了特定的调节任务而被识别，以及确定"好的”识别/性能折衷，传统上主要是经验和经验法则的问题，并且经常占据实践控制工程师的大部分时间。该项目提出了一种新的方法来开发实用的技术，可以帮助从业者探索识别/控制性能的权衡。其关键思想是建立在信号处理领域新颖而快速的“压缩感知”算法基础上，并将其与更传统的控制设计思想联合收割机相结合。这项研究将与施乐公司的科学家密切合作进行。的研究中心在韦伯斯特（纽约州），这将确保相关性和实用性的开发算法，并提供访问一个实际的静电印刷试验台。这种合作也将积极参与研究生。我们希望从这项研究中得到的工具，将广泛适用于工业控制设计的背景下。将通过在主要的控制会议上组织辅导讲习班和初步开发开放源码软件工具箱来促进这些工具的传播。