?!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> Robust Analysis of Consensus Algorithms-上海大学机电工程与自动化学院
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    Robust Analysis of Consensus Algorithms

    创建时间?nbsp; 2019-12-31  王智?    浏览次数?/span>



    报告? 纪成? 博士, 约翰霍普金斯大学

    邀请人: 任肖?教授

    Title: Robust Analysis of Consensus Algorithms

    Abstract: A fundamental problem in the networked dynamical system is to achieve the consensus in the presence of disturbance. In this talk, we present our recent progress towards developing distributed control strategies for the design of consensus systems. Our approach is to analytically evaluate the robust performance through the input-output norm of the dynamical system. A parametrized family of consensus algorithms is studied. In particular, we show that the input-output norm is finite unless the zero eigenvalue of the system is not observable from the output. Two special consensus applications, vehicle collision potential and sensor failure contingency analysis, are presented to illustrate our approach. We further propose two easy-to-implement approaches to improve consensus performance. In the first approach, which is referred to as the augmented consensus algorithm, a consensus filter is introduced in the feedback loop that transfers the consensus-oriented estimation to the system. In the second approach, which is referred to as the robust consensus algorithm, we add time-relative feedback from the current state to the initial state.

    Bio: Chengda Ji graduated from a joint program and received his Bachelor of Engineering and Bachelor of Science degrees in Mechanical Engineering from Beijing Institute of Technology, China, and Polytechnic University of Turin, Italy, respectively, in 2016. He then joined the Department of Mechanical Engineering at Johns Hopkins University as a research assistant with Professor Dennice Gayme as his Ph.D. advisor. His research interests include modeling, dynamics, and control of networked systems, e.g., power systems, vehicle platoons, and computer networks.  His current research focuses on developing distributed control and learning frameworks for networked systems. Chengda was awarded the Johns Hopkins Graduate Fellowship (2017) and CSC Undergraduate Fellowship (2015).

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