Cooperative Control: Stability, Optimality, Learning, and Games on Graphs:  Applications to Microgrid

October 04, 2013, Webb 1100

Frank Lewis

UT Arlington, Electrical Engineering


Distributed systems of multiple agents linked by communication networks only have access to information from their neighboring agents, yet must achieve global agreement on team activities to be performed cooperatively.  Examples include networked manufacturing systems, wireless sensor networks, networked feedback control systems, and the internet.  Sociobiological groups such as flocks, swarms, and herds have built-in mechanisms for cooperative control wherein each individual is influenced only by its nearest neighbors, yet the group achieves consensus behaviors such as heading alignment, leader following, exploration of the environment, and evasion of predators.  It was shown by Charles Darwin that local interactions between population groups over long time scales lead to global results such as the evolution of species. Natural decision systems incorporate notions of optimality, since the resources available to organisms and species are limited. In this talk we present design methods for cooperative controllers for distributed systems.  The developments are for general directed graph communication structures.  Cooperative control design is complicated by the fact that the graph topology properties limit what can be achieved by the local controller design.  Thus, local controller designs may work properly on some communication graph topologies yet fail on other topologies.  The relations between stability and optimality are far more intriguing for cooperative control systems than for standard control systems due to the intertwinings between agent dynamics and graph topology.  Optimality on Graphs.  Global optimal control of distributed systems on communication graphs is complicated by the fact that the resulting optimal control is generally not distributed in form. Therefore, it cannot generally be implemented on a prescribed communication graph topology by using only local neighbor information.  A condition is given for the existence of any optimal LQR controllers that can be implemented on a given graph in distributed fashion.  Given this condition, the performance index weighting matrices must be selected to depend on the graph structure. Graphical Games.  A novel form of multi-player game among agents in a communication graph is formulated where each agent is allowed to interact only with its neighbors.  A new notion of Interactive Nash equilibrium is defined that is suitable for graphical games and guarantees that that all agents achieve synchronization while optimizing their own value functions. Reinforcement Learning on Graphs.  This talk will discuss some new cooperative multi-agent learning methods for computing online the solutions to multi-player differential games on graphs. Techniques from Reinforcement Learning are used. Applications to Microgrid.  A new method for synchronization of heterogeneous non-identical cooperative systems is given based on feedback linearization.  The method allows improved secondary control of electric power microgrids for simultaneous frequency synchronization and distributed power balancing after a grid disconnection islanding event.

Speaker's Bio

F.L. Lewis, Fellow IEEE, Fellow IFAC, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer, is Distinguished Scholar Professor, Distinguished Teaching Professor, and Moncrief-O’Donnell Chair at The University of Texas at Arlington Research Institute.  IEEE Control Systems Society Distinguished Lecturer.  He obtained the Bachelor's Degree in Physics/EE and the MSEE at Rice University, the MS in Aeronautical Engineering from Univ. W. Florida, and the Ph.D. at Ga. Tech.  He works in feedback control, reinforcement learning, intelligent systems, and distributed control systems.  He is author of 6 U.S. patents, 273 journal papers, 375 conference papers, 15 books, 44 chapters, and 11 journal special issues.  He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, U.K. Inst Measurement & Control Honeywell Field Engineering Medal 2009.  Received IEEE Computational Intelligence Society Neural Networks Pioneer Award 2012.  Distinguished Foreign Scholar, Nanjing Univ. Science & Technology. Project 111 Professor at Northeastern University, China. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the Year by Ft. Worth IEEE Section.  Listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Dean’s Excellence in Doctoral Mentoring Award. Elected to UTA Academy of Distinguished Teachers 2012.  He served on the NAE Committee on Space Station in 1995.  Founding Member of the Board of Governors of the Mediterranean Control Association.  Helped win the IEEE Control Systems Society Best Chapter Award (as Founding Chairman of DFW Chapter), the National Sigma Xi Award for Outstanding Chapter (as President of UTA Chapter), and the US SBA Tibbets Award in 1996 (as Director of ARRI’s SBIR Program).