Synchronization of General Linear Multi‐Agent Systems With Measurement Noises

Liu, Wenhui; Yang, Chunjie; Deng, Feiqi; Liang, Jiarong

doi:10.1002/asjc.1357

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…The holding time L k is taken as k , where is a constant and > 1 may ensure the convergence of the average consensus algorithm. Based on the estimates of its own state and its neighbors' states, each node i designs the transmission of quantity v ij (t k+1 − 1) and v li (t k+1 − 1) by (4). Then, the update of node i will be realized by (3) at time t k+1 = t k + L k .…”

Section: Average Consensus Algorithmmentioning

confidence: 99%

“…Nevertheless, in physical applications, systems transfer information takes place via sensors, thus the transferred information is inevitable to be corrupted by noises or disturbances. By using the stochastic approximation algorithm, [4,11] studied the weak consensus and mean square consensus of distributed MAS with communication noises. In [11], a time-varying gain vector is applied to the inaccurate states of nodes due to the existence of communication noises.…”

Section: Introductionmentioning

confidence: 99%

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Average Consensus for Multi‐Agent System with Measurement Noise and Binary‐Valued Communication

Wang

et al. 2018

Asian Journal of Control

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This paper considers the average consensus problems of a class of multi-agent systems (MAS) with binary-valued communication. Each agent can only obtain its neighbor's binary-valued information under measurement noise because of limited bandwidth in communication channels. To seek consensus, we propose a two-scale multi-agent consensus algorithm with distributed strategy by combining state estimation and control design alternately. An exponential step size is chosen in the state estimation process and the estimation method can be proved to be asymptotically efficient. Additionally, by utilizing a distributed control law designed based on the estimates of the neighbors' states with a constant gain, we further prove that the proposed average consensus algorithm is convergent. Furthermore, the convergence speed of the proposed average consensus algorithm is given and proved. Finally, some simulation results, which illustrate the effectiveness of the obtained results, are also given in the paper.

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Section: Average Consensus Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Average Consensus for Multi‐Agent System with Measurement Noise and Binary‐Valued Communication

Wang

et al. 2018

Asian Journal of Control

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“…Undoubtedly, constructing a more adaptable stochastic model for multiple vehicle systems is an urgent task. The problem of Brownian motion-driven multiagent tracking was discussed in [21] and sufficient conditions for the tracking of multi-agents were obtained by using the auxiliary function of Brownian motion and random Ittrueo^ integral technology. A time lag multiagent system model with measurement noise was set up in [22], and the stability theory of stochastic differential equations was used.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with Multiplicative Noises

Zhao

Chen

et al. 2019

Sensors

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UAV Swarm with high dynamic configuration at a large scale requires a high-precision mathematical model to fully exploit its boundary performance. In order to instruct the engineering application with high confidence, uncertainties induced from either systematic measurement or the environment cannot be ignored. This paper investigates the I t o ^ stochastic model of the UAV Swarm system with multiplicative noises. By combining the cooperative kinematic model with a simplified individual dynamic model of fixed-wing-aircraft for the first time, the configuration control model is derived. Considering the uncertainties in actual flight, multiplicative noises are introduced to complete the I t o ^ stochastic model. Following that, the estimator and controller are designed to control the formation. The mean-square uniform boundedness condition of the proposed stochastic system is presented for the closed-loop system. In the simulation, the stochastic robustness analysis and design (SRAD) method is used to optimize the properties of the formation. More importantly, the effectiveness of the proposed model is also verified using real data of five unmanned aircrafts collected in outfield formation flight experiments.

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“…The dynamical analysis of coupled system has become a focal topic, particularly the synchronization phenomena. Some results on synchronization in various coupled lump parameter systems have been given in [26][27][28][29][30][31][32][33]. Although synchronization of the coupled LPS is studied popularly, synchronization of the coupled DPS is only investigated in [22] and [23].…”

Section: Introductionmentioning

confidence: 99%

Pinning Control of the Coupled Distributed Parameter System with Time Delay

Yuan

Zhang

Cao

2018

Asian Journal of Control

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In this paper, the synchronization for undirected and directed coupled distributed parameter systems (DPS) with delay by employing parabolic partial differential equation (PDE) theory and Lyapunov technique. In the case that the whole coupled system cannot synchronize by itself, the proportional-spatial derivative (P-sD) state feedback controller and the pinning scheme are designed to force the coupled DPS to synchronize. Finally, the effectiveness of the proposed control design methodology is demonstrated in numerical simulations.

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Synchronization of General Linear Multi‐Agent Systems With Measurement Noises

Cited by 13 publications

References 28 publications

Average Consensus for Multi‐Agent System with Measurement Noise and Binary‐Valued Communication

Average Consensus for Multi‐Agent System with Measurement Noise and Binary‐Valued Communication

Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with Multiplicative Noises

Pinning Control of the Coupled Distributed Parameter System with Time Delay

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