Output synchronization for heterogeneous networks of non-introspective agents

Grip, Håvard Fjær; Yang, Tao; Saberi, Ali; Stoorvogel, Anton A.

doi:10.1016/j.automatica.2012.06.081

Cited by 236 publications

(210 citation statements)

References 27 publications

(30 reference statements)

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“…In either case, the agents are assumed to be introspective, meaning that they have access to information about their own state or output in addition to the information received from the network. The authors have recently considered the more challenging case of heterogeneous non-introspective agents, and developed a methodology based on a distributed high-gain observer (Grip, Yang, Saberi, & Stoorvogel, 2012). However, like several other designs for heterogeneous networks (Wieland et al, 2011;, it was assumed that the agents can exchange internal controller states with neighboring agents in the network, in the same manner as in Li et al (2010).…”

Section: Introductionmentioning

confidence: 98%

Synchronization in networks of minimum-phase, non-introspective agents without exchange of controller states: Homogeneous, heterogeneous, and nonlinear

2015

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Section: Introductionmentioning

confidence: 98%

Synchronization in networks of minimum-phase, non-introspective agents without exchange of controller states: Homogeneous, heterogeneous, and nonlinear

2015

Self Cite

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“…In addition, the results in [28] make use of those in [20] and [26] to prove asymptotic synchronization over time-varying directed graphs that satisfy a uniform connectivity condition. The authors of [3] study the same consensus control problem over a fixed or timeinvariant graph topology. However it differs from [9,28] in that it considers heterogeneous nonintrospective agents.…”

Section: Introductionmentioning

confidence: 99%

“…Some recent results include [3,9,16,27,28] and references therein. The authors of [3,9,28] smartly bypass the difficulty posed by heterogeneity by embedding a homogeneous reference model into each agent's local controller. The state vectors of the local reference models are synchronized prior to achieving output consensus of the heterogeneous MAS.…”

Section: Introductionmentioning

confidence: 99%

“…The controller design is hinged on an observer that depends on a high gain parameter. It is important to highlight that in [3] the network graph is only present at the output but not in the input of the MAS. The case when the graph is present at the input introduces more difficulties.…”

Section: Introductionmentioning

confidence: 99%

“…Both features lower significantly the communication overhead between agents by avoiding the need to communicate the reference trajectory to all agents and by removing additional synchronization between local reference models. Furthermore we focus on non-introspective agents as in [3] and use only relative information for both state feedback and state estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Consensus Control for Heterogeneous Multiagent Systems

Alvergue¹,

Pandey²,

Gu³

et al. 2016

SIAM J. Control Optim.

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We study distributed output feedback control for a heterogeneous multi-agent system (MAS), consisting of N different continuous-time linear dynamical systems. For achieving output consensus, a virtual reference model is assumed to generate the desired trajectory that the MAS is required to track and synchronize. A distinct feature of our results lies in the local optimality and robustness achieved by our proposed consensus control algorithm. In addition our study is focused on the case when the available output measurements contain only relative information from the neighboring agents and reference signal. Indeed by exploiting properties of strictly positive real (SPR) transfer matrices, conditions are derived for the existence of distributed output feedback control protocols, and solutions are proposed to synthesize the stabilizing and consensus control protocol over a given connected digraph. It is shown that design techniques based on the LQG, LQG/LTR and H ∞ loop shaping can all be directly applied to synthesize the consensus output feedback control protocol, thereby ensuring the local optimality and stability robustness. Finally the reference trajectory is required to be transmitted to only one or a few agents and no local reference models are employed in the feedback controllers thereby eliminating synchronization of the local reference models. Our results complement the existing ones, and are illustrated by a numerical example.

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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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Output synchronization for heterogeneous networks of non-introspective agents

Cited by 236 publications

References 27 publications

Synchronization in networks of minimum-phase, non-introspective agents without exchange of controller states: Homogeneous, heterogeneous, and nonlinear

Synchronization in networks of minimum-phase, non-introspective agents without exchange of controller states: Homogeneous, heterogeneous, and nonlinear

Consensus Control for Heterogeneous Multiagent Systems

Consensus in Multi‐Agent Systems

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