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  5. Synchronization Control for Discrete-Time-Delayed Dynamical Networks With Switching Topology Under Actuator Saturations

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Article
English
2020

Synchronization Control for Discrete-Time-Delayed Dynamical Networks With Switching Topology Under Actuator Saturations

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English
2020
IEEE Transactions on Neural Networks and Learning Systems
Vol 32 (5)
DOI: 10.1109/tnnls.2020.2996094

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Qinglong Qinglong Han
Qinglong Qinglong Han

Swinburne University Of Technology

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Yonggang Chen
Zidong Wang
Jun Hu
+1 more

Abstract

This article is concerned with the synchronization control problem for a class of discrete-time dynamical networks with mixed delays and switching topology. The saturation phenomenon of physical actuators is specifically considered in designing feedback controllers. By exploring the mixed-delay-dependent sector conditions in combination with the piecewise Lyapunov-like functional and the average-dwell-time switching, a sufficient condition is first established under which all trajectories of the error dynamics are bounded for admissible initial conditions and nonzero external disturbances, while the l 2 - l ∞ performance constraint is satisfied. Furthermore, the exponential stability of the error dynamics is ensured for admissible initial conditions in the absence of disturbances. Second, by using some congruence transformations, the explicit condition guaranteeing the existence of desired controller gains is obtained in terms of the feasibility of a set of linear matrix inequalities. Then, three convex optimization problems are formulated regarding the disturbance tolerance, the l 2 - l ∞ performance, and the initial condition set, respectively. Finally, two simulation examples are given to show the effectiveness and merits of the proposed results.

How to cite this publication

Yonggang Chen, Zidong Wang, Jun Hu, Qinglong Qinglong Han (2020). Synchronization Control for Discrete-Time-Delayed Dynamical Networks With Switching Topology Under Actuator Saturations. IEEE Transactions on Neural Networks and Learning Systems, 32(5), pp. 2040-2053, DOI: 10.1109/tnnls.2020.2996094.

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Publication Details

Type

Article

Year

2020

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

IEEE Transactions on Neural Networks and Learning Systems

DOI

10.1109/tnnls.2020.2996094

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