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Abstract
Multi-agent systems are networks of interacting dynamic units that coordinate via information exchange to achieve team objectives such as formation flying or distributed computation. This course introduces modeling, analysis, and design using graph theory, dynamical systems, and optimization, covering stability, performance, distributed control, and multi-robot applications.
Objective
The course aims to develop a systematic understanding of multi-agent (networked) dynamic systems, focusing on how local interactions between agents give rise to coordinated global behavior. Students will learn to model such systems using graph theory and dynamical systems, analyze their properties including stability, convergence, and performance, and design distributed control strategies with an emphasis on consensus (agreement) and formation control protocols. By the end of the course, students should be able to apply these tools to real-world scenarios involving networked systems, including robotic formations and cooperative control problems.
Content
graph theory; algebraic and spectral graph theory; the consensus protocol (undirected, directed, switching, random); relative sensing networks (formation control, distributed estimation); analysis of networked system (stability, rate of convergence and the Fiedler eigenvalue, H2 and H∞ performance, controllability, observability); nonlinear models (Kuramoto model, interconnected passive systems); connectivity maintenance and maximization; rigid formations for control and localization; graph design for networked systems; Applications: formation control of quadrotors, attitude consensus for multiple satellites, autonomous vehicle swarms.
Resources
Literature
1. M. Mesbahi and M. Egerstedt, Graph Theoretic Methods in Multiagent Networks, Princeton University Press, 2010. 2. F. Bullo, Lectures on Network Systems, http://motion.me.ucsb.edu/book-lns , 2017. 3. J. Lunze, Networked Control of Multi-Agent Systems, 2019. 4. C. Godsil and G. Royle, Algebraic Graph Theory, Springer, 2009. 5. R. A. Horn and C. R. Johnson, Matrix Analysis, Cambridge University Press, 1990. 6. H. Bai, M. Arcak, and J. Wen, Cooperative Control Design: A Systematic, Passivity-based Approach, Springer, 2011. 7. W. Ren and R. Beard, Distributed Consensus in Multi-Vehicle Cooperative Control, Springer, 2008. 8. F. Bullo, J. Cortes, and S. Martinez, Distributed Control of Robotic Networks, Princeton University Press, 2009.
General Information
- Language
- English
- Levels
- MSC
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Coordination and Control of Multi-Agent Systems | No time listed | 3 h weekly |
Offered In
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Track: Electric Energy Engineering (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Electric Energy Engineering", see . The individual study plan is subject to the tutor's approval.)
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Specialisation Courses (These specialisation courses are particularly recommended for the area of "Energy and Power Electronics", but you are free to choose courses from any other field in agreement with your tutor. Semester / Research Projects are not allowed in this category. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.)
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