CAREER: Resilient and Scalable Framework for Control of Mutli-agent Systems: Ensemble Control of Network Motifs

This NSF CAREER project aims to develop a novel framework for control of multi-agent systems that is by nature resilient and scalable. The idea behind the framework is simple to state: instead of controlling a large network, one controls a large population of small ones. The project will bring transformative change for controlling large-scale complex systems by enabling the use of a single control input to simultaneously steer an arbitrarily large number of networked control systems.

This will be achieved by integrating both ensemble control theory and networked control theory. The intellectual merits of the project include developing an original set of methodologies and establishing novel control-theoretical results for controlling infinite ensembles of networked systems. The broader impacts of the project include establishing new connections between mathematics and the study of multi-agent systems, enabling the use of the proposed framework across various domains such as quantum systems, unmanned aerial systems, and robotics, and developing new courses that prepare the next generation of students with advanced mathematical tools and engineering methods.

To establish the proposed control framework, the PI will investigate ensemble systems that are composed of infinitely many networked control systems. The individual networked systems in the ensemble may share common information flow topologies, but show variations in system parameters which are indexed by points in a continuum parameterization space.

A major technical challenge is that one can only use a finite-dimensional, common control input to steer a continuum ensemble of individual control systems. Moreover, these individual systems are constrained by information flow topologies. Such a challenge has not yet been tackled in the literature.

Thus, to develop the proposed control framework, the PI will: (1) establish new concepts, such as ensembles of motifs, and formulate new problems, such as structural ensemble controllability; (2) establish necessary and/or sufficient conditions for information flow topologies of individual networked systems that can comprise a controllable ensemble; (3) establish a deep understanding of the fundamental relation between physical dynamics of individual systems, information flow topology, and geometry of parameterization space for ensemble controllability and for various types of output controllability.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.