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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=747140
• 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大大增加了社会、商业、金融和工业部门内部和之间数据的可获得性和存储以及信息的传输。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确实在各个应用领域变得越来越普遍。虽然数据越来越多，但也迫切需要对其进行仔细的开发。越来越需要开发基于数据的方法来实现系统自动化和最优化。随着基于学习的控制器设计技术的发展，如强化学习(RL)、迭代学习控制(ILC)和极值搜索控制(ESC)，控制专家们现在拥有了越来越多的无模型控制器设计技术，可以在缺乏过程动力学精确知识的情况下解决复杂的控制问题。尽管它们有明显的共同点，但领先的无模型学习控制和优化技术几乎是相互独立地发展起来的。他说，拟议的研究计划涉及三个主题，重点是无模型技术的应用。第一个主题考虑了用于解决复杂系统中的实时优化问题的ESC技术的发展。第二个主题考虑了在具有未知动态和不确定通信网络结构的不可靠对等通信网络上运行的分布式优化系统的设计。最后一个主题考虑了学习技术和无模型控制技术的协同作用。其目标是通过利用学习方法的共性以及ESC等领先的无模型技术的稳定性和收敛证书来扩大这些数据驱动技术的应用范围。ESC现在已广泛应用于工业，并在从电子显微镜到太空探索中的遥感等各种具有挑战性的领域享有越来越多的应用。该项目提出的研究进一步扩展了ESC技术在处理具有复杂动态的大系统方面的适用性。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大型系统中能源效率、能源利用和存储领域的应用。据介绍，该研究计划提出了与工业应用直接相关的控制工程和学习技术方面的重大贡献。三名博士和两名硕士将在该计划中接受培训。它为HQP在对工业具有战略意义的控制和优化领域的培训提供了一个独特的机会。