Optimal design of stochastic networked control systems

提出的研究目标是促进对不确定性和信息约束下的网络化和协作控制系统的理解。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，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了解。拟议的研究将有三个主要的技术和实际贡献：(i)发展关于在信息限制下分散动态系统性能的数量理论。这将导致设计原则的实际系统和基础设施设计为各种各样的应用程序。（ii）通过事件驱动通信的复杂连接系统的随机稳定性和此类系统稳定性的随机漂移准则的发展。这对于只有在资源可用时才能进行通信的分散系统非常重要。（iii）当控制器能够通过部分信息或训练序列学习系统动力学时，产生近似最优控制策略。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。其结果将允许设计自适应、认知和资源高效的分散和网络化控制系统。除了对不确定条件下决策的理论问题做出基础性贡献外，鉴于信息约束下网络化控制系统的优化工程系统设计，研究成果将在加拿大及其他技术领域产生重要的实际影响。此外，四名博士、两名硕士和两名本科生将在该提案下得到严格的培训，成为独立的研究人员，为加拿大的研究和技术界做出贡献。

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. The outcomes will allow for the design of adaptive, cognitive and resource-efficient decentralized and networked control systems. In addition to fundamental contributions on theoretical problems regarding decision making under uncertainty, the research outcomes will have important practical impact in the technology sector in Canada and beyond, in view of optimal engineering system design for networked control systems under information constraints. In addition, four PhD, two MSc and two undergraduate students will be trained rigorously under this proposal, and be independent researchers who will benefit Canada's research and technological community.