An internal model principle is necessary and sufficient for linear output synchronization

Wieland, Peter; Sepulchre, Rodolphe; Allgöwer, Frank

doi:10.1016/j.automatica.2011.01.081

Cited by 848 publications

(656 citation statements)

References 15 publications

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“…As in the consensus problem the objective is to synchronize the system to a common trajectory by exchanging relative information [10], [22], [23]. Recently, some efforts have been made to understand consensus and synchronization problems in which both the individual dynamics as well as communication constraints play an important role [27], [15], [21], [29].…”

Section: Introductionmentioning

confidence: 99%

A linear consensus approach to quality-fair video delivery

Col

Tarbouriech

Zaccarian

et al. 2014

53rd IEEE Conference on Decision and Control

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Section: Introductionmentioning

confidence: 99%

A linear consensus approach to quality-fair video delivery

Col

Tarbouriech

Zaccarian

et al. 2014

53rd IEEE Conference on Decision and Control

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“…This is motivated by the approach that is proposed in [16] where the existence of an internal model is both a necessary and sufficient condition for the output synchronization of linear networked systems. Let us discuss the result of [16] (see also [2], Section 3.6) adapted to passive linear systems. Given N heterogeneous passive linear systemṡ…”

Section: Output Synchronization Via Internal Modelmentioning

confidence: 99%

“…Excluding the trivial case in which the closed-loop system has an attractive set of equilibria where the outputs are all zero, the authors of [16] show that the output synchronization problem for N heterogeneous systems is solvable if and only if there exist matrices S, C w such that lim t→∞ ||y i (t)− C w e −St w 0 || = 0 for each i ∈ {1, 2, . .…”

Section: Output Synchronization Via Internal Modelmentioning

confidence: 99%

“…The controllers (15)- (16) are a modified form of the ones in [16,Eq. (10)] where in the latter, the local controller communicates the entire exosystem state w i to its connecting nodes and the local controller uses both state-feedback and state-observer.…”

Section: Output Synchronization Via Internal Modelmentioning

confidence: 99%

“…In this paper, we relax the strict incremental passivity assumption of each subsystem by considering controllers which incorporate exosystems which communicate with each other. This is motivated by the recent results in [16] where the existence of an internal model is both necessary and sufficient condition for output synchronization of linear networked systems.the results of [16] can be extended to the nonlinear case. In particular, we propose a distributed control law that uses the local information of the networked exosystems and the relative output measurements of the subsystems and of the exosystems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the internal model principle in formation control and in output synchronization of nonlinear systems

Persis

Jayawardhana

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-The role of internal model principle is investigated in this paper in the context of collective synchronization and formation control problems. In the collective synchronization problem for nonlinear systems, we propose distributed control laws for passive systems which synchronize to the solution of an incrementally passive exosystem. This generalizes the result from linear systems where the existence of an internal model is required for the output synchronization of networked systems. In our second result, a distributed control law that solves a formation control problem for incrementally passive systems is proposed based also on the internal model principle.

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Consensus in Multi‐Agent Systems

Proskurnikov

Cao

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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Many cooperative behaviors of multi‐agent teams emerge from local interactions among the agents, where an agent interacts with a few “adjacent” teammates, but has no information about the remaining agents. For instance, the self‐organization of many biological populations –including swarms of insects, flocks of birds, and schools of fish –are based on such local interaction rules: the motion and decisions of an individual agent are determined by the behavior of its nearest neighbors in the population. A special case of multi‐agent coordination is consensus , that is, the agreement of agents on some quantity of interest or, more generally, the full or partial synchronization of their state trajectories. Establishing consensus is a “benchmark” problem in multi‐agent systems study, which allows to reveal the main principles of multi‐agent coordination and, in particular, the role of the system's interaction graph (or topology). Consensus lies in the heart of many natural phenomena (e.g., synchronous oscillation of neural cells, which maintains a stable heart rhythm) and engineering designs (e.g., attitude synchronization of satellites). In this article, we give a brief review of distributed consensus algorithms, focusing on the basic ideas and relevant mathematical theory, in particular, graph‐theoretic methods.

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An internal model principle is necessary and sufficient for linear output synchronization

Cited by 848 publications

References 15 publications

A linear consensus approach to quality-fair video delivery

A linear consensus approach to quality-fair video delivery

On the internal model principle in formation control and in output synchronization of nonlinear systems

Consensus in Multi‐Agent Systems

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