Distributed Model Predictive Control of Large-scale, Networked Systems

大规模网络系统的分布式模型预测控制

• 批准号: 0456694
• 负责人: James Rawlings
• 金额: --
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2009-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0456694&HistoricalAwards=false
• 关键词: Distributed; Model; Predictive; Control; Large

ABSTRACTPI: James Rawlings Institution: University of WisconsinProposal Number: 0456694Title: Distributed model predictive control of large-scale networked systemsThe control of large complex networked systems may be accomplished by applying local modeling and control techniques to its smaller, more manageable constituent subsystems. Because there is little cooperation between the local controllers, they can interact in unexpected ways, not considered in the design phase. As a result, the full system may display fragility and even instability in the face of unmodeled disturbances. An excellent illustration of this phenomenon was the failure of the North American power system in August 2003.The PIs plan to develop new methods for the control and optimization of large, complex, networked systems. They will design these methods to be more robust than existing methods in the presence of large disturbances and component or subsystem failure, with performance approaching that of a fully centralized methodology. At the same time, these methods will be implementable in practical settings, by taking advantage of the currently deployed subsystem models and controllers, and avoiding the onerous modeling requirements and organizational/institutional obstacles associated with a centralized control methodology. The key to the approaches will be communication of information between subsystems, and cooperation between their controllers. The forecasts produced by model predictive control techniques provide rich information about future behavior of each subsystem. In some situations, sharing of this information between subsystems is itself sufficient to achieve almost all of the potential benefits of centralized control. In more tightly coupled systems, more extensive cooperation is required. One form of cooperation is to give the subsystem controllers a common objective-a straightforward modification if the subsystems already use model predictive control. If the subsystems currently use PID or some other low-level control scheme, it is not difficult to first replace these controllers with model predictive controllers before applying the techniques developed in this research. Cooperation can also involve sharing of information between subsystems, possibly more than once within a single sample period, with local reoptimizations performed after each exchange of information. A crucial component of the project will be the design of robust and rapidly converging optimization schemes that perform most of their computations locally within each subsystem and exchange limited information between subsystems.To ensure industrial relevance and impact, the PIs have established collaborations with six industrial partners to demonstrate these new approaches on two economically significant application classes: electric power networks and large-scale, integrated chemical plants exchanging raw materials and products. They plan to collaborate with the industrial partners to test and refine the proposed methods, to demonstrate the benefits with actual industrial operating data, and to transfer the technology into practice. Testing and implementation of the proposed methods will provide a vital educational experience for the graduate students supported by the project.The broader impact lies in the opportunity to demonstrate methods that increase the reliability of critical infrastructures that are composed of many highly interacting subsystems. Infrastructure of this type is already becoming pervasive in highly technological societies, and the need for improving the reliability of this infrastructure is increasingly urgent.

摘要：詹姆斯·罗林斯研究所：威斯康星大学建议号：0456694标题：分布式模型预测控制的大规模网络系统的控制大型复杂的网络系统可以通过应用本地建模和控制技术，其较小的，更易于管理的组成子系统。 由于本地控制器之间几乎没有合作，它们可以以意想不到的方式进行交互，而在设计阶段没有考虑到。因此，整个系统可能会显示脆弱性，甚至不稳定，在面对未建模的干扰。2003年8月北美电力系统的故障是这一现象的一个很好的例证。PI计划开发新的方法来控制和优化大型、复杂的网络系统。他们将设计这些方法，使其在存在大干扰和组件或子系统故障的情况下比现有方法更鲁棒，性能接近完全集中式方法。同时，这些方法将在实际环境中实施，通过利用当前部署的子系统模型和控制器，并避免与集中控制方法相关的繁重的建模要求和组织/机构障碍。 这些方法的关键将是子系统之间的信息通信，以及它们的控制器之间的合作。由模型预测控制技术产生的预测提供关于每个子系统的未来行为的丰富信息。在某些情况下，在子系统之间共享这些信息本身就足以实现集中控制的几乎所有潜在好处。 在更紧密耦合的系统中，需要更广泛的合作。合作的一种形式是给子系统控制器一个共同的目标-一个简单的修改，如果子系统已经使用模型预测控制。如果子系统目前使用PID或其他一些低级别的控制方案，这是不难的，首先取代这些控制器与模型预测控制器应用本研究中开发的技术之前。合作还可以涉及子系统之间的信息共享，在单个采样周期内可能不止一次，在每次信息交换之后进行局部再优化。该项目的一个关键组成部分是设计强大且快速收敛的优化方案，这些方案在每个子系统内本地执行大部分计算，并在子系统之间交换有限的信息。为了确保工业相关性和影响力，PI与六个工业合作伙伴建立了合作关系，以在两个具有经济意义的应用类别上展示这些新方法：电力网络和大型综合化工厂交换原材料和产品。他们计划与工业合作伙伴合作，测试和改进所提出的方法，用实际的工业运行数据证明其好处，并将技术转化为实践。测试和实施所提出的方法将提供一个重要的教育经验，为研究生的项目所支持的，更广泛的影响在于有机会展示的方法，增加了由许多高度相互作用的子系统组成的关键基础设施的可靠性。这类基础设施在高技术社会中已变得普遍，提高这类基础设施可靠性的需要日益迫切。