LEARNING-BASED DISTRIBUTED CONTROL

#distinguished, #Distributed #Control #Learning
Share

Distributed control is a classical research topic. While a rich theory is available, some assumptions such as availability of subsystem dynamics and topology and the subsystems following the prescribed controllers exactly have proven difficult to remove. An interesting direction in recent times to get away from these assumptions has been the utilization of learning for control. In this talk, we consider some problems in control design for distributed systems using learning. Our core message is that utilizing control-relevant properties in learning algorithms can not only guarantee concerns such as stability, performance, safety, and robustness that are important in control of physical systems, but also help with issues such as data sparsity and sample complexity that are concerns during the implementation of learning algorithms.



  Date and Time

  Location

  Hosts

  Registration



  • Date: 23 Oct 2024
  • Time: 12:00 PM to 01:00 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
  • Add_To_Calendar_icon Add Event to Calendar
If you are not a robot, please complete the ReCAPTCHA to display virtual attendance info.
  • Contact Event Host
  • Co-sponsored by Temple University Electrical and Computer Engineering Department
  • Starts 31 August 2024 12:00 AM
  • Ends 23 October 2024 09:00 AM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
  • No Admission Charge


  Speakers

Vijay of Electrical and Computer Engineering Department

Topic:

LEARNING-BASED DISTRIBUTED CONTROL

Distributed control is a classical research topic. While a rich theory is available, some assumptions such as availability of subsystem dynamics and topology and the subsystems following the prescribed controllers exactly have proven difficult to remove. An interesting direction in recent times to get away from these assumptions has been the utilization of learning for control. In this talk, we consider some problems in control design for distributed systems using learning. Our core message is that utilizing control-relevant properties in learning algorithms can not only guarantee concerns such as stability, performance, safety, and robustness that are important in control of physical systems, but also help with issues such as data sparsity and sample complexity that are concerns during the implementation of learning algorithms.

Biography:

Vijay Gupta is the Elmore Professor of Electrical and Computer Engineering and the Associate Head for Graduate and Professional Programs in ECE at the Purdue University, having joined the faculty in May 2022. He received his B. Tech degree at Indian Institute of Technology, Delhi, and his M.S. and Ph.D. at California Institute of Technology, all in Electrical Engineering. He previously served as a research associate in the Institute for Systems Research at the University of Maryland, College Park, a consultant at the United Technologies Research Center, and as a faculty member, the Associate Chair and the Director for Graduate Studies in the Department of Electrical Engineering at Notre Dame. He is a Fellow of IEEE and has received the 2018 Antonio Ruberti Young Research Award from the IEEE Control Systems Society, the 2013 Donald P. Eckman Award from the American Automatic Control Council, and a 2009 National Science Foundation (NSF) CAREER Award. Gupta's research and teaching interests are at the interface of learning, game theory, and distributed systems, with applications to transportation networks, power grid, and parallel computing.

Email:

Address:Elmore Family School of Electrical and Computer Engineering, Materials and Electrical Engineering Building, 501 Northwestern Ave., West Lafayette, Indiana, United States, 47907-2035