Probabilistic-Constrained Distributed Set-Membership Estimation Over Sensor Networks: A Dynamic Periodic Event-Triggered Approach

This article is concerned with the event-triggered probabilistic-constrained distributed set-membership estimation problem for a discrete-time nonlinear system over sensor networks. For saving communication resource, a novel discrete-time dynamic periodic event-triggered mechanism (ETM) is first dev...

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Veröffentlicht in:IEEE transactions on network science and engineering Jg. 9; H. 6; S. 4444 - 4457
Hauptverfasser: Xie, Yuhan, Ding, Sanbo, Yang, Feisheng, Wang, Leimin, Xie, Xiangpeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Co-design
Computational geometry
Constraints
Convexity
Discrete time systems
discrete-time dynamic periodic event-triggered mechanism
distributed set-membership estimation
Ellipsoids
Estimation
Network topology
Nonlinear systems
Optimization
probabilistic constraints
Probabilistic logic
Probability theory
Robot sensing systems
sensor networks
Sensors
Sparse matrices
Statistical analysis
ISSN:2327-4697, 2334-329X
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Zusammenfassung:This article is concerned with the event-triggered probabilistic-constrained distributed set-membership estimation problem for a discrete-time nonlinear system over sensor networks. For saving communication resource, a novel discrete-time dynamic periodic event-triggered mechanism (ETM) is first developed for the sensor network. Under the proposed method, the sensor node calculates the ETM in a periodic manner and the threshold is adjusted dynamically. Thereafter, a distributed set-membership estimator is constructed, and a probability-based estimated ellipsoidal constraint is put forward to acquire a more flexible set-membership estimation algorithm. Simultaneously, an auxiliary-function-dependent approach is proposed to derive the criterion for the co-design of the probabilistic-constrained set-membership estimator and the event-triggered parameter such that the system states reside in the estimated ellipsoid with a pre-specified probability. The auxiliary function is constructed in a piecewise style aiming to deal with the sawtooth constraint of sampling signals. Furthermore, a recursive convex optimization algorithm with regard to the estimated ellipsoid is presented. Finally, a simulation example is employed to verify the validity of the developed method.
Bibliographie:ObjectType-Article-1
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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2022.3201395