Edge-based strict Lyapunov functions for consensus with connectivity preservation over directed graphs

Restrepo, Esteban; Lorı́a, Antonio; Sarras, Ioannis; Marzat, Julien

doi:10.1016/j.automatica.2021.109812

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…where c 2 > 0, η i is as in (18), ν * hi = ν * hi + Vi , and γ is a positive control gain to be defined.…”

Section: A Control Design For the Hard Proximity And Safety Constraintsmentioning

confidence: 99%

“…. Now, as observed in [18], using an appropriate labeling of the edges, the incidence matrix can be expressed as E = [ E t E c ] where E t ∈ R N ×(N −1) denotes the full columnrank incidence matrix corresponding to an arbitrary spanning tree G t ⊂ G and E c ∈ R N ×(M −N +1) represents the incidence matrix corresponding to the remaining edges in G\G t . Similarly, the edge states may be split as…”

Section: External Dynamics In Edge-based Coordinatesmentioning

confidence: 99%

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Tracking Control of Cooperative Marine Vehicles Under Hard and Soft Constraints

Restrepo,

Matouš,

Pettersen

2024

IEEE Trans. Control Netw. Syst.

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We solve the tracking-in-formation problem for a group of underactuated autonomous marine vehicles interconnected over a directed topology. The agents are subject to hard inter-agent constraints, i.e. connectivity maintenance and collision avoidance, and soft constraints, specifically on the non-negativity of the surge velocity, as well as to constant disturbances in the form of unknown ocean currents. The control approach is based on an inputoutput feedback linearization for marine vehicles and on the edge-based framework for multi-agent consensus under constraints. We establish almost-everywhere uniform asymptotic stability of the output dynamics with guaranteed respect of the constraints. High-fidelity simulations are provided to illustrate our results.

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“…where c 2 > 0, η i is as in (18), ν * hi = ν * hi + Vi , and γ is a positive control gain to be defined.…”

Section: A Control Design For the Hard Proximity And Safety Constraintsmentioning

confidence: 99%

Section: External Dynamics In Edge-based Coordinatesmentioning

confidence: 99%

Tracking Control of Cooperative Marine Vehicles Under Hard and Soft Constraints

Restrepo,

Matouš,

Pettersen

2024

IEEE Trans. Control Netw. Syst.

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“…In detail, the control objective for system (1) is to develop an adaptive event‐triggered strategy such that the consensus of MASs and connectivity preservation are achieved and sampling/execution is reduced. To achieve the desired objective, the following assumption is imposed on an initial communication graph, which is clearly basic and common in the related works (see e.g., References 15–25 and references therein).…”

Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

“…If the distances exceed a certain range, the connectivity will no longer preserve and thus consensus can not be achieved. A lot of results [15][16][17][18][19][20][21][22][23][24][25][26] have been obtained on connectivity-preserving consensus of MASs, while only a few 21,24 allow for uncertainties and the control coefficients therein are known. Actually, in the adaptive setting, enforcing the relative distances within the limited range is difficult for the connectivity.…”

Section: Introductionmentioning

confidence: 99%

Connectivity‐preserving consensus: An adaptive event‐triggered strategy

Guo,

Liu

2023

Intl J Robust & Nonlinear

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SummaryThe connectivity of communication graph is indispensable for consensus of multi‐agent systems (MASs), which in many applications (e.g., wireless sensor) depends on the relative distances between agents. But, in the adaptive setting particularly with system nonlinearities and event‐triggered communication, it is rather difficult to enforce the relative distances within the limited range for the connectivity. This paper focuses on developing an adaptive event‐triggered control strategy with connectivity preservation in the context of nonidentical unknown control coefficients and heterogeneous nonlinearities coupling with parameter uncertainties. First, a group of potential functions are introduced acting as control barrier functions to constrain the relative distances between agents within the limited range for all time. Also, two dynamic gains are specialized for each agent to handle the system uncertainties, system nonlinearities and negative effect of the execution error. Then, an adaptive event‐triggered protocol is designed for each agent such that the connectivity‐preserving consensus of MASs is achieved and Zeno behavior is excluded. Moreover, an extended study is conducted on a leader‐following scenario. Two simulation examples illustrate the effectiveness of the proposed event‐triggered control strategy.

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Synchronization of second-order Kuramoto networks from the perspective of edge dynamics

Chen

2023

Control Theory Technol.

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Edge-based strict Lyapunov functions for consensus with connectivity preservation over directed graphs

Cited by 13 publications

References 21 publications

Tracking Control of Cooperative Marine Vehicles Under Hard and Soft Constraints

Tracking Control of Cooperative Marine Vehicles Under Hard and Soft Constraints

Connectivity‐preserving consensus: An adaptive event‐triggered strategy

Synchronization of second-order Kuramoto networks from the perspective of edge dynamics

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