Moving Horizon Estimation for Networked Systems With Quantized Measurements and Packet Dropouts

Liu, Andong; Yu, Li; Zhang, Wen-An; Chen, M. Z. Q.

doi:10.1109/tcsi.2012.2226499

Cited by 67 publications

(29 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In future work, the problem will be further investigated. In addition, the approaches proposed in this paper can be also used to the networked systems in [42][43][44][45], Markovian jump parameters [46] and fault-tolerant control systems [47][48][49].…”

Section: Resultsmentioning

confidence: 99%

New mean square exponential stability condition of stochastic fuzzy neural networks

Xing

Pan

et al. 2015

Neurocomputing

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

New mean square exponential stability condition of stochastic fuzzy neural networks

Xing

Pan

et al. 2015

Neurocomputing

View full text Add to dashboard Cite

“…where Φ(t) is the state transition matrix of system (1). From (14) to (17), the conclusion of Theorem 1 can be obtained.…”

Section: Proofmentioning

confidence: 85%

“…If the relative motions among agents are asymptotically stable, then consensus is achieved, which has been extensively investigated in the literature (e.g., Refs. [1][2][3][4][5][6][7][8][9][10][11]). The absolute motion, which is usually described by a consensus function, represents the agreement state of all agents when consensus is achieved.…”

Section: Introductionmentioning

confidence: 99%

Uniqueness of consensus functions for time-delayed swarm systems with time-varying topologies

Yang

et al. 2015

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

“…As shown in Liu et al (2013), methods based on Kalman filtering may become suboptimal or even unstable when dealing with the random delay. This motivated the development of moving horizon estimation (MHE) schemes that can guarantee the observer convergence and stability in the time delay system.…”

Section: O O P E R At I V E Lo C a L I S At I O N W I T H C O M M Umentioning

confidence: 99%

Moving Horizon Estimation for Cooperative Localisation with Communication Delay

et al. 2014

View full text Add to dashboard Cite

Cooperative Localisation (CL) technology is required in some situations for Multiple Unmanned Underwater Vehicle (MUUVs) missions. During the CL process, the Relative Localisation Information (RLI) of the master UUV is transmitted to slave UUVs via acoustic communication. In the underwater environment, the RLI is subject to a random time delay. Considering the time delay characteristic of the RLI during the acoustic communication, a Moving Horizon Estimation (MHE) method with a Delayed Extended Kalman Filter (DEKF)-based arrival cost update law is presented in this paper to obtain an accurate and reliable estimation of present location. Additionally, an effective computation method for the MHE method is employed, in which the "Lower Upper" (LU) factorization is used to compute the solution of the Karush-Kuhn-Tucker (KKT) system. At the end of this paper, simulation results are presented to prove the superiority and practicality of the proposed MHE algorithm. K E Y WO R D S 1. Multiple Unmanned Underwater Vehicles (MUUVs).

show abstract

Moving Horizon Estimation for Networked Systems With Quantized Measurements and Packet Dropouts

Cited by 67 publications

References 41 publications

New mean square exponential stability condition of stochastic fuzzy neural networks

New mean square exponential stability condition of stochastic fuzzy neural networks

Uniqueness of consensus functions for time-delayed swarm systems with time-varying topologies

Moving Horizon Estimation for Cooperative Localisation with Communication Delay

Contact Info

Product

Resources

About