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Control and learning for networked systems

Dates:21 November 2023
Times:13:00 - 14:00
What is it:Seminar
Organiser:Department of Electrical and Electronic Engineering
Who is it for:University staff, External researchers, Current University students
Speaker:Dr Zhongguo Li
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  • Department of Electrical and Electronic Engineering
  • Control systems
  • Past seminars

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  • In category "Seminar"
  • In group "(EEE) Control and robotics"
  • In group "(EEE) Seminar series"
  • By Department of Electrical and Electronic Engineering

Join in this EEE open seminar from the department's new academics.

Network-connected systems have received significant research attention over the past two decades, especially, in the domains of optimisation learning and control. Distributed algorithms have been developed aiming at facilitating decision-making at the local level while accomplishing certain global tasks through network communications. The interaction between learning and control plays an important role in achieving intelligent operation for robotic systems in challenging environment. This presentation will focus on the recent advancement in the concept of reciprocal learning and control and its application in autonomous search problems.

Speaker

Dr Zhongguo Li

Role: Lecturer in Robotics, Control, Communication and AI

Organisation: Department of Electrical and Electronic Engineering, University of Manchester

Biography: Zhongguo Li received both B.Eng. and Ph.D. degrees in electrical and electronic engineering from the University of Manchester, Manchester, U.K., in 2017 and 2021, respectively. He is currently a Lecturer in Robotics, Control, Communication and AI with the Department of Electrical and Electronic Engineering at the University of Manchester. Before joining Manchester, he was a Lecturer in Robotics and AI at University College London and a Research Associate at Loughborough University. His research interests include optimisation and decision-making for advanced control, distributed algorithm development for game and learning in network connected multi-agent systems, and their applications in robotics and autonomous vehicles.

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