Distributed Model Predictive Control of Large-scale, Networked Systems

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对其较小的、更易于管理的组成子系统应用局部建模和控制技术来完成。由于本地控制器之间几乎没有合作，因此它们可以以意想不到的方式进行交互，这在设计阶段是没有考虑到的。因此，在面对未建模的干扰时，整个系统可能会表现出脆弱性甚至不稳定性。2003年8月北美电力系统的故障就是这一现象的绝佳例证。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健壮，性能接近完全集中的方法。同时，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需求和组织/制度障碍，这些方法将在实际设置中实现。这些方法的关键将是子系统之间的信息通信和控制器之间的合作。由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，子系统之间的信息共享本身就足以实现集中控制的几乎所有潜在好处。在耦合更紧密的系统中，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，这是一个简单的修改。如果子系统目前使用PID或其他一些低级控制方案，在应用本研究开发的技术之前，首先用模型预测控制器替换这些控制器并不困难。合作还可以涉及子系统之间的信息共享，可能在单个采样周期内不止一次，在每次信息交换之后执行局部重新优化。该项目的一个关键组成部分将是设计健壮且快速收敛的优化方案，该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，pi与六个工业伙伴建立了合作关系，在电力网络和交换原材料和产品的大型综合化工厂这两个具有经济意义的应用类别上展示这些新方法。他们计划与工业伙伴合作，测试和完善建议的方法，用实际的工业操作数据证明其好处，并将技术转化为实践。测试和实施所提出的方法将为该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增加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的方法。这种类型的基础设施在高科技社会中已经变得普遍，提高这种基础设施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.