时间：05月20日（周五）下午2:00

腾讯会议：743-191-814

主持人：陈博

报告一题目：“Cooperative Perception and Control with a Distributed Scheme”

摘要：

Thanks to the rapid development of perception, communication and computing technologies, intelligent systems have increasingly strong autonomous capabilities and are becoming a strategic research direction in many fields. By leveraging information sharing and collaboration among individuals, multi-agent systems are expected to achieve high-level swarm intelligence for more complex application scenarios. To achieve this goal, one fundamental research issue is cooperative state estimator and controller synthesis for multi-agent systems. Current cooperation modes usually adopt a distributed framework owing to its strong robustness and high scalability. However, distributed frameworks may degrade the system performance compared with centralized frameworks due to the lack of global information. Therefore, with the popularization of multi-agent systems in various fields, it is essential to figure out the specific performance gap between distributed and centralized frameworks, in order to guarantee the cooperation performance quantitatively. In this talk, first, a performance evaluation index is introduced. Then, several closed-form expressions of performance gaps between typical distributed and centralized frameworks are established, which can facilitate the design of fusion steps in the distributed framework. Finally, some novel results regarding cooperative behavior of multi-agent systems are discussed.

报告人简介：

段培虎，香港大学博士后。2015年6月毕业于华中科技大学，获机械工程学士学位，随后就读于北京大学，并于2020年7月获力学系统与控制博士学位。2019年5月至2019年8月，任香港城市大学研究助理。2020年10月至2021年8月香港科技大学电子与计算机工程系博士后。2021年9月至今香港大学电气与电子工程系博士后。主要从事协同控制和分布式状态估计研究，在Automatica和IEEE Trans.等期刊上发表高水平论文10余篇。担任IEEE Trans.等国际10余个期刊审稿人。

报告二题目：“Scenario optimization and data-driven stability analysis of switched linear systems: Probabilistic guarantees and sample complexity”

摘要：

Data-driven methods have been quite a success in handling complex dynamical systems. In this presentation, I will talk about a fundamental mathematical tool for hypothesis tests and probabilistic guarantees, called scenario optimization (also known as scenario approach). This technique has already been successfully applied to a wide range of uncertain systems. In particular, I will show how it can be used to analyze the stability of a special class of hybrid systems, called switched linear systems, which consist of a finite set of linear dynamics (called modes) and a switching rule that indicates the current active mode of the system. Identification of such systems is often challenging due to hybrid behaviors, except for small systems with a small number of modes. To deal with this challenge, we propose data-driven methods in which we do not require a dynamical model. In the spirit of scenario optimization, probabilistic stability guarantees are derived relying on a finite set of observations of system trajectories.

报告人简介：

Bio. Zheming Wang is a postdoctoral research in the department of mathematical engineering at UCLouvain in Belgium. He is broadly interested in the area of control theory and optimization. His recent research interests focus on rigorous data-driven methods for analyzing and controlling complex systems with formal guarantees. He received a B.S. degree in Mechanical Engineering from Shanghai Jiao Tong University, China, in 2012, and a Ph.D. degree in Mechanical Engineering from National University of Singapore in 2016.