Resilient Control Systems for Industrial Process Automation

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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.