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

这个探索性研究（EAGER）研究项目的早期概念拨款的目标是复杂的多输入多输出系统的控制设计，该系统具有非常大的状态空间维度，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是可识别的，但需要一个耗时或昂贵的过程来估计。这类植物涵盖了大量的工业和网络物理系统，从化学过程到制造系统，再到高质量的多机静电复制系统，它们构成了我们工作的主要驱动应用和试验台。对于这样的系统，精确的模型识别是可能的，但成本高昂，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”？为特定的监管任务进行识别，并确定“良好”的识别/性能权衡，传统上主要是经验和经验法则的问题，并且通常占据了实践控制工程师时间的很大一部分。这个项目提出了一种开发实用技术的新方法，可以帮助从业者探索识别/控制性能权衡。关键思想是建立在信号处理领域新颖而快速的“压缩感知”算法的基础上，并将其与更传统的控制设计思想相结合。这项研究将与施乐公司的科学家密切合作进行。该中心将确保所开发算法的相关性和实际适用性，并提供使用实际静电印刷试验台的机会。这项合作还将积极吸引一名研究生。我们期望这项研究所产生的工具能广泛应用于工业控制设计环境。将通过在主要的控制会议上组织指导讲习班和初步开发一个开放源码软件工具箱来促进它们的传播。

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.