Complex engineered systems that can adapt to their environments while maintaining safety guarantees are crucial in many applications including Internet-of-Things, transportation, and electric power systems. The primary objective of this project is to develop a scalable design methodology to control very large collections of systems to achieve common objectives despite cyber and physical constraints. As an application, the electric load control problem, in which the goal is to coordinate the power consumption of thousands of small electric loads like air conditioners and refrigerators to help the grid balance supply and demand without inconveniencing electricity consumers and while respecting the physical limitations of the power distribution network, will be considered. The research results will support the integration of more wind and solar power, improving the grid's environmental and health impacts. Education and outreach activities will involve K-12, undergraduate, and graduate students along with stakeholders from local power companies. The key characteristics of the problems considered are a large number of dynamically almost decoupled systems. Each system has their local requirements and constraints and they are coupled through requirements about their collective behavior. A bi-level control architecture will be developed that can handle soft performance requirements and allow adaptability at the upper-level, and that guarantees the satisfaction of hard safety requirements at the lower-level. The lower-level will exploit structural properties symmetries of the systems and requirements, in particular, permutation invariance, to enable highly scalable synthesis methods to ensure safety. The upper-level will leverage adaptation/learning to improve system performance when control inputs are overridden for the purpose of safety.
Performance Period: 01/01/2019 - 12/31/2023
Institution: University of Michigan Ann Arbor
Sponsor: National Science Foundation
Award Number: 1837680