Distributed Model Predictive Control of Large-scale, Networked Systems

提要：詹姆斯·罗林斯研究所：威斯康星大学提议编号：0456694题目：大规模网络系统的分布式模型预测控制大型复杂网络系统的控制可以通过将本地建模和控制技术应用于其更小、更易于管理的组成子系统来完成。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交互，而不是在设计阶段考虑。因此，面对未建模的扰动，整个系统可能会表现出脆弱性，甚至不稳定。2003年8月北美电力系统的故障就是这一现象的一个很好的例证。PIS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部件或子系统故障的情况下比现有方法更稳健，性能接近完全集中的方法。同时，通过利用目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/体制障碍，这些方法将可在实际环境中实施。这些方法的关键将是子系统之间的信息交流，以及它们的控制器之间的合作。模型预测控制技术产生的预测提供了关于每个子系统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--如果子系统已经使用模型预测控制，则进行直接的修改。如果子系统目前使用的是PID或其他一些低层控制方案，那么在应用本研究开发的技术之前，首先将这些控制器替换为模型预测控制器并不困难。合作还可涉及各子系统之间的信息共享，可能在一个采样期内不止一次，并在每次信息交换后进行当地再优化。该项目的一个关键组成部分将是设计稳健和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。为了确保行业相关性和影响，PI已与六个工业合作伙伴建立合作，在两个具有重要经济意义的应用类别上演示这些新方法：电力网络和交换原材料和产品的大型综合化工厂。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来展示其好处，并将技术转化为实践。测试和实施建议的方法将为项目支持的研究生提供重要的教育经验。更广泛的影响在于有机会演示提高由许多高度交互的子系统组成的关键基础设施的可靠性的方法。这种类型的基础设施已经在高科技社会中变得无处不在，提高这种基础设施的可靠性的需求日益迫切。

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.