Optimal design of stochastic networked control systems

随机网络控制系统的优化设计

• 批准号: 355788-2013
• 负责人: Yuksel, Serdar
• 金额: $ 1.75万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=527893
• 关键词: Optimal; design; stochastic; networked; control

The goal of the proposed research is to advance the understanding of networked and cooperative control systems under uncertainty and information constraints. Stochastic networked control systems arise in many practical settings (such as in decentralized and adaptive power regulation in the energy/power grid, automotive applications for collision avoidance, sensor networks and in economics theory). However, there are many fundamental and practical aspects yet to be understood regarding such systems. The proposed research will have three main technical and practical contributions: (i) Development of a quantitative theory on the performance of decentralized dynamic systems given information constraints. This will lead to design principles for practical systems and infrastructure design for a large variety of applications. (ii) Stochastic stability of complex connected systems through event-driven communications and development of stochastic drift criteria for stability of such systems. This is important for decentralized systems which can only communicate when resources are available. (iii) Generation of approximately optimal control policies when controllers are able to learn the dynamics of a system through partial information or training sequences. This is especially important in cognition and learning aspects of controllers for adapting to an unknown environment in which they live and act.

提出的研究的目标是推进不确定性和信息约束下的网络和合作控制系统的理解。随机网络控制系统出现在许多实际环境中（例如在能源/电网中的分散和自适应功率调节，用于避免碰撞的汽车应用，传感器网络和经济学理论）。然而，关于这种系统，还有许多基本的和实际的方面有待理解。拟议的研究将有三个主要的技术和实际贡献：（一）分散的动态系统的信息约束的性能的定量理论的发展。这将导致实际系统的设计原则和基础设施设计的各种应用程序。(ii)通过事件驱动的通信实现复杂连接系统的随机稳定性，并发展此类系统稳定性的随机漂移准则。这对于去中心化系统很重要，因为它们只能在资源可用时进行通信。(iii)当控制器能够通过部分信息或训练序列学习系统的动态时，近似最优控制策略的生成。这在控制器的认知和学习方面尤其重要，以适应他们生活和行动的未知环境。