About the speaker
Silvia Ferrari is John Brancaccio Professor of Mechanical and Aerospace Engineering at Cornell University. Prior to that, she was Professor of Engineering and Computer Science at Duke University, and Founder and Director of the NSF Integrative Graduate Education and Research Traineeship (IGERT) and Fellowship program on Wireless Intelligent Sensor Networks (WISeNet). Currently, she is the Director of the Laboratory for Intelligent Systems and Controls (LISC) at Cornell University and the co-Director of the Věho Institute for Vehicle Intelligence at the Cornell Tech. Her principal research interests include active perception, robust adaptive control, learning and approximate dynamic programming, and control of multiscale dynamical systems. She is the author of the book “Information-driven Path Planning and Control,” MIT Press (2021), and of the TED talk “Do robots dreams of electric sheep?”. She received the B.S. degree from Embry–Riddle Aeronautical University and the M.A. and Ph.D. degrees from Princeton University. She is a senior member of the IEEE, and a member of ASME, SPIE, and AIAA. She is the recipient of the ONR young investigator award (2004), the NSF CAREER award (2005), and the Presidential Early Career Award for Scientists and Engineers (PECASE) award (2006).
Abstract
Collaborative agents, such as unmanned ground, aerial, and underwater vehicles or robots equipped with on-board wireless communication devices and sensors are becoming crucial to both civilian and military applications because of their ability to replace or assist humans in carrying out dangerous yet vital missions. This talk discusses a general framework for the optimal control of multiscale dynamical systems comprised of many collaborative agents. The distributed optimal control (DOC) approach presented is developed for applications in which the performance of the multiscale dynamical system can be represented in terms of a restriction operator, such as a probability density function, that maps the microscopic agent state into a macroscopic state. The evolution of the restriction operator is then optimized subject to a macroscopic description provided by the continuity equation. To date, DOC optimality conditions have been derived using Calculus of Variations, and both direct and indirect methods of solution have been developed and demonstrated for multiagent path planning and control. This talk will review these recent developments and discuss future research directions involving robust and adaptive control for multiagent systems, and the control of multiple teams of heterogeneous agents.