Events Calendar

From Systems to Networks: Theory and Computation for Distributed Predictive Control

Abstract: The control of a network of interacting dynamical systems is a central challenge for addressing a range of emerging application problems; examples include energy systems balancing a network of generation, load and storage devices, or robotic systems comprising a large number of components or agents. Utilizing the connectivity and interactions in the network by exploiting advances in communication and computation technologies offers the potential for pushing these systems to higher performance while increasing efficiency of operation, which will reduce system over-design and associated costs. However, safety requirements and high system complexity represent key limiting factors for leveraging these new opportunities.
This talk will present some of our recent work that brings high-performance control with hard guarantees on system safety to distributed systems, offering a scalable and modular approach that exploits interconnection effects and flexibly adjusts to network changes. A new framework for plug and play distributed predictive control will be introduced and we will discuss essential theoretical and practical aspects for certifying distributed decision-making based on an optimization-in-the-loop paradigm. We will show how the proposed scheme ensures the fundamental properties of stability and constraint satisfaction of the global system without recourse to any centralized coordination and even in the presence of online network changes, while allowing the control systems to optimize for performance. Application examples in area generation control and grid-aware electric vehicle charging will demonstrate the capabilities of the proposed theory. Lastly, we will address the computational aspects of the framework and present new results for certifying optimization with limited-precision computation or communication.

Biography: Melanie Zeilinger is a Postdoctoral Researcher and Marie Curie fellow in a joint program with the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley and the Empirical Inference Department at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. From 2011-2012 she was a postdoctoral fellow at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. She received the Ph.D. degree in Electrical Engineering from ETH Zurich in Switzerland in 2011, and the diploma in Engineering Cybernetics from the University of Stuttgart in Germany in 2006. She conducted her diploma thesis research at the University of California at Santa Barbara in 2005-2006. She received the ETH medal for her dissertation in 2012 and was awarded a Marie Curie Fellowship for Career Development by the European Commission in 2011. Her research interests are centered around real-time and distributed control and optimization, as well as safe learning-based control, with applications to energy distribution and management systems and human-in-the-loop control.
Host: Urbashi Mitra, ubli@usc.edu, EEB 540, x04667

Location: EEB 248

Audiences: Everyone Is Invited

Contact: Kaela Berry