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Designing Fully Distributed Adaptive Event-Triggered Controllers for Networked Linear Systems With Matched Uncertainties.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Dec2019, Vol. 30 Issue 12, p3645-3655, 11p
- Publication Year :
- 2019
-
Abstract
- This paper considers the distributed event-triggered consensus control problem for a network of linear systems subject to bounded uncertainties satisfying the matching condition. Due to the existence of nonidentical uncertainties, the multiagent system studied in this paper is essentially heterogeneous, and the event-triggered consensus problem of which is much more challenging than that of homogeneous linear networks in the existing works. We propose a static nonsmooth event-triggered protocol that includes a nonlinear term to ensure that consensus is achieved and the Zeno behavior is excluded. To avoid the undesirable chattering effect caused by the nonsmooth protocol, we design a static continuous event-based protocol, which can guarantee that the consensus error is ultimately bounded and the upper bound of the consensus error can be made satisfactorily small by choosing properly the design parameters. We also design a continuous adaptive event-triggered protocol that includes time-varying weights into both the control law and the triggering function. Contrary to the event-triggered protocols in the previous related works, the adaptive event-based protocol is fully distributed and scalable, whose design does not require any global information of the network graph. Besides, all the event-triggered protocols in this paper do not need continuous communications among neighboring agents in either control laws’ updating or triggering conditions’ monitoring. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 30
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 140336703
- Full Text :
- https://doi.org/10.1109/TNNLS.2018.2868986