Optimization and Control in Large-scale Uncertain Processes

大规模不确定过程的优化与控制

• 批准号: RGPIN-2019-05205
• 负责人: Guay, Martin
• 金额: $ 2.84万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715281
• 关键词: Optimization; Control; Large; scale; Uncertain

Technological advances on sensing equipment, communication networks and computational hardware have contributed to a dramatic increase in the availability and storage of data and the transmission of information both within and between the social, commercial, financial and industrial sectors. The interaction between information management systems, data transmission networks, and systems in the physical world is indeed becoming more and more prevalent in various application domains. Data is not only increasingly available, there is also an urgent need for its careful exploitation. There is a growing need for the development of data-based methodologies for system automation and optimization. With the advent of recent developments in learning-based controller design techniques, such as reinforcement learning (RL), iterative learning control (ILC) and extremum-seeking control (ESC), control experts are now equipped with a growing set of model-free controller design techniques that can solve complex control problems in the absence of exact knowledge of process dynamics. Despite their obvious commonalities, leading model-free learning control and optimization techniques have evolved almost independently of each other. The proposed research program tackles three themes focussed on the application of model-free techniques. The first theme considers the development of ESC techniques for the solution of real-time optimization problems in complex systems. The second theme considers the design of distributed optimization systems operated over unreliable peer-to-peer communication networks subject to unknown dynamics and uncertain communication network structure. The last theme considers the synergy of learning techniques and model-free control techniques. The goal is to expand the scope of application of these data-driven techniques by exploiting the generality of learning approaches and the stability and convergence certificates of leading model-free techniques such as ESC. ESC is now widely used in industry and enjoys a growing list of applications in diverse challenging fields from electron microscopy to remote sensing in space exploration. The research proposed in this program further extends the applicability of ESC techniques to tackle large-scale systems with complex dynamics. The use of learning technology further expands the application of these techniques to achieve optimal performance in systems operating in uncertain environments. Of particular interests are applications in the area of energy efficiency, energy utilization and storage in large-scale systems. The research program proposes significant contributions in control engineering and learning technology that are directly relevant in industrial applications. Three PhD and two MSc are to be trained in this program. It provides a unique opportunity for the training of HQP in the fields of control and optimization that are strategic to industry.

传感设备、通信网络和计算硬件方面的技术进步促使社会、商业、金融和工业部门内部和之间的数据可用性和存储以及信息传输大幅增加。信息管理系统、数据传输网络和物理世界中的系统之间的交互在各个应用领域中确实变得越来越普遍。 数据不仅越来越容易获得，而且迫切需要认真利用。越来越需要开发用于系统自动化和优化的基于数据的方法。随着基于学习的控制器设计技术的最新发展，如强化学习（RL），迭代学习控制（ILC）和极值搜索控制（ESC），控制专家现在配备了越来越多的无模型控制器设计技术，可以解决复杂的控制问题，在没有确切的过程动态知识。尽管它们有明显的共性，但领先的无模型学习控制和优化技术几乎是相互独立发展的。 拟议的研究计划处理三个主题集中在无模型技术的应用。第一个主题考虑ESC技术的发展，在复杂系统中的实时优化问题的解决方案。第二个主题是考虑在不可靠的对等通信网络上运行的分布式优化系统的设计，该网络具有未知的动态特性和不确定的通信网络结构。最后一个主题考虑学习技术和无模型控制技术的协同作用。我们的目标是通过利用学习方法的通用性以及领先的无模型技术（如ESC）的稳定性和收敛性证书来扩大这些数据驱动技术的应用范围。 电喷雾干燥技术在工业上得到了广泛的应用，从电子显微镜到空间探测中的遥感技术，其应用范围越来越广。 该计划中提出的研究进一步扩展了ESC技术的适用性，以解决具有复杂动态的大规模系统。学习技术的使用进一步扩展了这些技术的应用，以实现在不确定环境中运行的系统的最佳性能。特别令人感兴趣的是在能源效率、能源利用和大规模系统储存领域的应用。 该研究计划在控制工程和学习技术方面做出了重大贡献，这些技术与工业应用直接相关。三个博士和两个硕士将在这个计划中培养。它为HQP在控制和优化领域的培训提供了一个独特的机会，这些领域对行业具有战略意义。