Distributed dissipativity and graph theoretic properties in distributed economic MPC

分布式经济 MPC 中的分布式耗散性和图论特性

• 批准号: 244600449
• 负责人: Professor Dr.-Ing. Frank Allgöwer
• 金额: --
• 依托单位: Institut für Regelungstechnik (IRT)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/244600449?language=en
• 关键词: Distributed; dissipativity; graph; theoretic; properties

The ability to control large networks of dynamical systems by local controlactions is an imperative prerequisite for the functionality of numerous modern engineering systems. Examples of such systems are smart grids, which use distributed control to integrate renewable energy sources into a network of intelligent" generators and loads; intelligent factories, which improve the production capabilities by controlling the transportation and manufacturing devices; or autonomous mobile systems, where groups of mobile agents have to perform complex tasks cooperatively. Despite the natural differences inherent to these envisioned systems, engineers working on any of them are all faced with one fundamental challenge: The dynamical systems (e.g., generators, production units, robots, etc.) have to be controlled in a way such that the control actions applied to a single system are consistent with the control actions applied to the other systems.This project proposes a constructive approach to address the above challenges within the distributed economic model predictive control framework. For this a novel model predictive control scheme will bedeveloped to solve control problems involving several distributed decision makers, all equipped with individual cost functions. We aim to develop algorithmic methods with guaranteed systems theoretic properties to ensure fairness and efficiency in such distributed and multi-objective set-ups. The key contribution of this project will be a connection between numerical distributed optimization algorithms and distributed model predictive control schemes. These results will provide a novel understanding of the requirements that distributed model predictive control imposes on the underlying numerical optimization algorithms.The present project is complemented by a partner project, proposed by Prof. Lars Grüne (University of Bayreuth). Together, the two projects will address the challenges of distributed economic model predictive control from a broad basis, and investigate conceptual as well as practical issues, ranging from a performance analysis up to numerical realizations.

通过局部控制动作控制大型动力系统网络的能力是许多现代工程系统功能的必要先决条件。这种系统的例子是智能电网，它使用分布式控制将可再生能源整合到智能“发电机和负载”网络中；智能工厂，通过控制运输和制造设备来提高生产能力；或者自主移动系统，其中一组移动代理必须协同执行复杂的任务。尽管这些设想的系统存在固有的自然差异，但从事其中任何一种工作的工程师都面临着一个基本挑战：动态系统（例如，发电机，生产单元，机器人等）必须以一种方式进行控制，使应用于单个系统的控制动作与应用于其他系统的控制动作一致。本项目提出了一种在分布式经济模型预测控制框架内解决上述挑战的建设性方法。为此，将开发一种新的模型预测控制方案来解决涉及多个分布式决策者的控制问题，这些决策者都配备了单独的成本函数。我们的目标是开发具有保证系统理论性质的算法方法，以确保这种分布式和多目标设置的公平性和效率。本项目的关键贡献将是数字分布式优化算法与分布式模型预测控制方案之间的联系。这些结果将为分布式模型预测控制对底层数值优化算法的要求提供新的理解。本项目由Lars gr<s:1> ne教授（拜罗伊特大学）提出的一个伙伴项目补充。这两个项目将共同从广泛的基础上解决分布式经济模型预测控制的挑战，并研究从性能分析到数值实现的概念和实际问题。