Event/Self-Triggered Control for Leader-Following Consensus Over Unreliable Network With DoS Attacks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3137-3149 |
Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 30 |
Issue number | 10 |
Online published | 23 Jan 2019 |
Publication status | Published - Oct 2019 |
Link(s)
Abstract
This paper investigates the leader-following consensus issue with event/self-triggered schemes under an unreliable network environment. First, we characterize network communication and control protocol update in the presence of denial-of-service (DoS) attacks. In this situation, an event-triggered communication scheme is first proposed to effectively schedule information transmission over the network possibly subject to malicious attacks. In this communication framework, synchronous and asynchronous updated strategies of control protocols are constructed to achieve leader-following consensus in the presence of DoS attacks. Moreover, to further reduce the cost induced by event detection, a self-triggered communication scheme is proposed in which the next triggering instant can be determined by computing with the most updated information. Finally, a numerical example is provided to verify the effectiveness of the proposed communication schemes and updated strategies in the unreliable network environment.
Research Area(s)
- Denial-of-service (DoS) attack, Denial-of-service attack, event triggered, Information processing, Multi-agent systems, multiagent system, Probability distribution, Protocols, self-triggered., Urban areas
Citation Format(s)
Event/Self-Triggered Control for Leader-Following Consensus Over Unreliable Network With DoS Attacks. / Xu, Wenying; Ho, Daniel W. C.; Zhong, Jie et al.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 10, 10.2019, p. 3137-3149.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 10, 10.2019, p. 3137-3149.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review