Resilient Control Systems for Industrial Process Automation

用于工业过程自动化的弹性控制系统

• 批准号: RGPIN-2022-03687
• 负责人: AlDabbagh, Ahmad
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 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=761747
• 关键词: Resilient; Control; Systems; Industrial; Process

Digital control and monitoring units automate the regulation of physical processes, while a communication network is used to transfer information. As needed, human operators manually regulate the processes by interacting with the monitoring units. In this practical setup for industrial process automation, process dynamics evolve in continuous-time, whereas control, monitoring, communication, and operator action dynamics evolve in discrete-time, with periodic, time-driven, and event-triggered events. These different dynamics and couplings give rise to a new and challenging class of systems - the cyber-physical-human systems - which complicate the development of control systems, especially for large-scale distributed applications with multiple physically interconnected processes and multiple interconnected control and monitoring units. What further complicates the matter is when abnormal events occur (e.g., faults and cyber attacks), requiring timely response from control systems. The objective of the proposed research program is to discover new design and analytical methods for control systems that offer resiliency to abnormality in industrial process automation (namely, resilient control systems). The program focuses on developing a large-scale interconnected system paradigm with a cyber-physical-human system basis. This paradigm will capture the dependency between the involved physical processes, digital systems and signals, and human operator actions, modelled in a hybrid manner with continuous- and discrete-time dynamics. It will be the most comprehensive representation of modern control systems globally, and will effectively model industrial applications in our society, such as in manufacturing plants, oil & gas refineries, and water distribution networks. The research will uncover novel solutions for fault diagnosis, cyber security, and alarm management, which will advance our ability to develop fully resilient and autonomous industrial process automation. New computational methods and tools will be developed to detect random faults and malicious cyber attacks, locate their exact location, and determine control procedures to mitigate their effects. The research will build on control, optimization, and graph theories, and will address state estimation, controller and observer designs, and system stabilization and performance criteria. When abnormal events like faults and cyber attacks are detected, they are annunciated on monitoring units as alarm messages, to alert human operators and direct their actions. A key aim of the research program is to develop algorithmic procedures to better manage the display of alarms and automate human operator actions. To arrive at such fundamental results yet practical solutions, the research will develop data mining and machine learning procedures. This novel research program will advance knowledge and train personnel to deliver innovative contributions for the control systems community and industry.

数字控制和监控单元自动调节物理过程，而通信网络用于传输信息。根据需要，人类操作员通过与监控单元交互来手动调节过程。在这个工业过程自动化的实际设置中，过程动态在连续时间内演变，而控制，监控，通信和操作员动作动态在离散时间内演变，具有周期性，时间驱动和事件触发的事件。这些不同的动力学和耦合产生了一类新的和具有挑战性的系统-网络物理人系统-这使得控制系统的开发复杂化，特别是对于具有多个物理互连过程和多个互连控制和监视单元的大规模分布式应用。使问题进一步复杂化的是当异常事件发生时（例如，故障和网络攻击），需要控制系统及时响应。拟议的研究计划的目的是发现新的设计和分析方法，为控制系统，提供弹性异常的工业过程自动化（即弹性控制系统）。该计划的重点是开发一个具有网络物理人类系统基础的大规模互联系统范例。这种范式将捕获所涉及的物理过程，数字系统和信号，以及人类操作员的行为之间的依赖关系，以连续和离散时间动态的混合方式建模。它将是全球现代控制系统最全面的代表，并将有效地模拟我们社会中的工业应用，例如制造工厂，石油和天然气炼油厂以及供水网络。该研究将揭示故障诊断，网络安全和警报管理的新解决方案，这将提高我们开发完全弹性和自主工业过程自动化的能力。将开发新的计算方法和工具来检测随机故障和恶意网络攻击，定位其确切位置，并确定控制程序以减轻其影响。该研究将建立在控制，优化和图论，并将解决状态估计，控制器和观测器的设计，系统稳定和性能标准。当检测到故障和网络攻击等异常事件时，它们会在监控单元上发出警报消息，以提醒操作人员并指导他们的行动。该研究计划的一个主要目标是开发算法程序，以更好地管理警报的显示和自动化人类操作员的行动。为了达到这些基本的结果，但实际的解决方案，研究将开发数据挖掘和机器学习程序。这项新的研究计划将促进知识和培训人员，为控制系统社区和行业做出创新贡献。