Abstract

Cooperative autonomy based on multi-agent systems (MASs) is of great academic, industrial and military interest, which highlights the ever-increasing importance of distributed control design. If asked to single out the most fundamental design challenge, researchers would, probably unanimously, point to the distributed information availability within an MAS network – an agent can only communicate with its neighbors, and the limited local information impedes the making of wiser cooperative control decisions. This stimulates an interesting question: Will it be possible for an agent to perceive its non-neighbors without even having to speak with them? In giving a positive answer, this talk will present a new concept called distributed global awareness, which is defined as the capacity of an agent to infer about its non-neighbors using local data and build an awareness of the global MAS. The discussion will focus on how to develop data-driven distributed estimation methods to turn this concept into reality and how to exploit it to enhance MAS-related control, especially for leader-follower tracking, average consensus and distributed optimization. Centering these topics, the talk will also sketch the prospects, opportunities and challenges that may drive future research.

Bio

Huazhen Fang received his Ph.D., M.Sc. and B.Eng. from the University of California, San Diego (Mechanical Engineering, 2014), University of Saskatchewan, Canada (Mechanical Engineering, 2009), and Northwestern Polytechnic University, China (Computer Science, 2006). He is currently an Assistant Professor of Mechanical Engineering at the University of Kansas, where he joined in 2014 and has led the Information & Smart Systems Laboratory. His research interests lie in control and estimation theory with application to energy management, cooperative robotics and system prognostics. He has published over 50 peer-reviewed journal and conference articles and invented/co-invented three U.S. patents. He has received the 2019 NSF CAREER Award and at the University of Kansas, the 2017 Miller Scholar Award for Research Excellence and 2016 Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award. He actively contributes professional service to the communities and was selected as an Outstanding Reviewer or Reviewer of the Year by Automatica, IEEE Transactions on Cybernetics, and ASME Journal of Dynamic Systems, Measurement and Control.