Graph Theoretic Methods in Multiagent Networks

By Mehran Mesbahi, Magnus Egerstedt. 2010

2,877 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

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Citations in the corpus

Concurrent learning for convergence in adaptive control without persistency of excitation (2010, methodology): “Such gradient based parameter update laws have been used for NN training [36], in system identification, and in decentralized control of networked robots [27].”
Safety Barrier Certificates for Collisions-Free Multirobot Systems (2017, methodology): “Thus, what is ultimately deployed on the system is a combination of a “formally” designed nominal controller together with a “hand-crafted” collision avoidance algorithm, as discussed in [4], [5], [7], with the basic idea being that the multirobot system executes the nominal, primary controller most”
Distributed continuous-time online optimization using saddle-point methods (2016, background): “(5) Note that this assumption ensures that λ2, also known as the Fiedler eigenvalue of L, is always strictly positive [14].”
Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems (2015, background): “The following concepts of graph theory can be found in Mesbahi & Egerstedt (2010).”
Consensus in multi-agent systems: a review (2021, background): “Linear systems can only achieve asymptotic convergence, and usually there is no guarantee for reaching consensus in a finite time (Mesbahi and Egerstedt 2010); while in reality, and according to system theory, it is necessary to have controllers that can reach consensus in the least possible time.”
Distributed Matrix Scaling and Application to Average Consensus in Directed Graphs (2013, background): “…IEEE The problem of obtaining a doubly stochastic matrix and the distributed solutions proposed in this paper are related to matrix scaling problems, which have been addressed in the context of nonnegative matrices [14]–[16] (the comparison of these works with ours is deferred to Section III-B).”
Decentralized Consensus Control of a Rigid-Body Spacecraft Formation with Communication Delay (2016, background): “This is a standard assumption for digraphs where every pair of separate vertices are connected through a directed path [25,26].”
Continuous and discontinuous opinion dynamics with bounded confidence (2012, background): “Indeed, graph theory provides an effective tool to model interactions between agents and its use is becoming common both in engineering [4, 17] and in economics and social sciences [11].”
Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design (2015, neutral): “We provide [Bullo et al., 2009, Ren and Beard, 2008, Mesbahi and Egerstedt, 2010, OlfatiSaber et al., 2007] and the references therein as a starting point for the interested reader.”
Differentially private iterative synchronous consensus (2012, background): “ry and sucient conditions for achieving consensus with synchronous mechanisms, including cases where the matrix P is time-varying, have been studied in [21, 17] (see the book for a complete overview [14]). Sucient conditions for achieving consensus with message delays and losses has been developed i”
Event based agreement protocols for multi-agent networks (2013, neutral): “…for multi-agent systems by research papers (Arcak, 2007; Cortés, 2008; Jadbabaie, Lin, & Morse, 2003; Lin, Broucke, & Francis, 2004; Moreau, 2005; Olfati-Saber & Murray, 2004; Tanner, Jadbabaie, & Pappas, 2007; Xiao & Wang, 2008) and monographs (Mesbahi & Egerstedt, 2010; Ren & Beard, 2008).”
On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions (2014, background): “…formulated as that of a group of agents exchanging information with the objective of reaching a common decision, a consensus, by resorting to distributed algorithms that make use of the information that each agent collects from neighboring agents (see, e.g. [2, 11, 12, 15, 19, 20, 21, 24, 25, 27].”
Distributed Seeking of Nash Equilibria With Applications to Mobile Sensor Networks (2012, methodology): “, [29], [30]) with time-varying gains ( ), so that we can choose standard Lyapunov function applied to these problems (see, e.”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, background): “ed to the original balanced truncation literature [23]. More generally, sensor and actuator placement may be optimized for robustness [113, 114,115,116], or for network control and consensus problems [117,118,119,120,121,122,123,124]. The sensor placement algorithms discussed above are rooted ﬁrmly ”