Title Distributed Control of Networked Multi-agent Systems: Algorithms and Applications Biography Wei Ren is currently a Professor with the Department of Electrical and Computer Engineering, University of California, Riverside. He received the Ph.D. degree in Electrical Engineering from Brigham Young University, Provo, UT, in 2004. Prior to joining UC Riverside, he was a faculty member at Utah State University and a postdoctoral research associate at the University of Maryland, College Park. His research focuses on distributed control of multi-agent systems and autonomous control of unmanned vehicles. Dr. Ren was a recipient of the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize in 2017 and the National Science Foundation CAREER Award in 2008. He is an IEEE Fellow and an IEEE Control Systems Society Distinguished Lecturer. He is an Associate Editor for Automatica and IEEE Transactions on Automatic Control. Abstract While autonomous agents that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous agents operating in a coordinated fashion. Potential applications for networked multiple autonomous agents include environmental monitoring, search and rescue, space-based interferometers and hazardous material handling. Networked multi-agent systems place high demands on features such as low cost, high adaptivity and scalability, increased flexibility, great robustness, and easy maintenance. To meet these demands, the current trend is to design distributed control algorithms that rely on only local interaction to achieve global group behavior. The purpose of this talk is to overview our research in distributed control of networked multi-agent systems. Theoretical results on distributed leaderless consensus with agent dynamics including first- and second-order linear dynamics, rigid body attitude dynamics, and Euler-Lagrange dynamics, distributed single-leader collective tracking with reduced interaction and partial measurements, distributed multi-leader containment control with local interaction, distributed average tracking with multiple time-varying reference signals, and distributed optimization with non-identical constraints will be introduced. Application examples in multi-vehicle cooperative control will also be introduced.