| Time: | June 14, 2016 |
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Dipl.-Ing. Jingbo Wu
Institute for Systems Theory and Automatic Control
University of Stuttgart,
Stuttgart, Germany
Tuesday 2016-06-14 16:00
IST-Seminar-Room V9.22 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
Abstract
In synchronization and coordination problems of groups of non-identical agents, it is commonly assumed that either full-state information is available, or each agent obtains local measurements, such that it is able to estimate its own state individually.
However, in some cases such as relative sensing networks this does not hold true, leaving the question open, how the individual observers can deliver the information needed to perform synchronization or coordination. Especially, since relative sensing is an important feature for many multi-agent systems in applications such as formation control and localization, this issue needs to be thoroughly studied.
One way to address this problem is to let the observers communicate with some prescribed communication topology, in order to create a meaningful estimate. Subsequently, designing the cooperative observers depending on the communication topology becomes a task, which we propose to solve by an H∞-type approach. This leads to a separable optimization problem, where the solution delivers robust observers that are able to attenuate or reject disturbances. Furthermore, using these methods, the same type of performance guarantees can also be given for the closed-loop synchronization or coordination, respectively.
Biographical Information
Jingbo Wu received the Diploma degree in engineering cybernetics from the University of Stuttgart, Germany, in December 2010. During his undergraduate studies, he spent an academic year at the University of Toronto, Canada. After graduation, in 2011, he joined the Institute for Systems Theory and Automatic Control, University of Stuttgart as a Ph.D. student. His main research interest lies in the field of of Cooperative Control and Estimation, where specifically he works on distributed algorithms for estimation and regulation of heterogeneous multi-agent systems.