Thesis Defense:  Managing Information in Networked and Multi-Agent Control Systems

Michael Epstein

Abstract:  In recent years the field of Networked Control Systems (NCS) has emerged to describe situations where information in feedback control loops is passed through imperfect communication channels that can result in quantized, delayed and even lost information. The research in this field focuses on quantifying  performance degradations in the presence of network effects and proposing algorithms for managing the information flow to counter those negative effects. In this thesis I propose and analyze algorithms for managing information flow for several NCS  scenarios; state estimation with lossy measurement signals, using input buffers to reduce the frequency of communication with a remote plant, and performing state estimation when control signals are transmitted to a remote plant via a lossy communication link with no  acknowledgment signal at the estimator. Multi-agent coordinated control systems serve as a prime example of an emerging area of feedback control systems that utilize feedback loops with information passed through possibly imperfect communication  networks.  In these systems, agents use a communication network to exchange information in order to achieve a desired global objective. Hence managing the information flow has a direct impact on the performance of the system. I also explore this area by focusing on  the problem of multi-agent average consensus. I propose an algorithm based on a hierarchical decomposition of the communication topology to speed up the time to convergence. For all these topics I focus on designing intuitive algorithms that intelligently manage the  information flow and provide analysis and simulations to illustrate their effectiveness.