Design Fixed-Time Practical Distributed Average Tracking Algorithms for Nonlinear Signals With Bounded- and Lipschitz-Type Derivatives

In this brief, the fixed-time practical distributed average tracking (DAT) problem for multiple nonlinear signals is studied, where the nonlinear signals are with bounded- and Lipschitz-type derivatives respectively. Two fixed-time practical DAT algorithms are proposed for agents with local interact...

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Vydáno v:IEEE transactions on circuits and systems. II, Express briefs Ročník 67; číslo 12; s. 3103 - 3107
Hlavní autoři: Chen, Qiang, Shi, Guoqing, Zhao, Yu, Wen, Guanghui
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Automation
Circuits and systems
Convergence
DAT algorithm
Derivatives
fixed time
Generators
Initial conditions
Isotopes
Laplace equations
Lipschitz-type derivative
Multi-agent systems
Time dependence
time-base generator
Topology
Tracking
ISSN:1549-7747, 1558-3791
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Shrnutí:In this brief, the fixed-time practical distributed average tracking (DAT) problem for multiple nonlinear signals is studied, where the nonlinear signals are with bounded- and Lipschitz-type derivatives respectively. Two fixed-time practical DAT algorithms are proposed for agents with local interactions to track the average of the multiple nonlinear signals within a fixed convergence time by using time-base generator techniques. Different from existing results, the proposed DAT algorithms in this brief are applicable to DAT problems for nonlinear signals with both bounded- and Lipschitz-type derivatives, which keeps more reality. Also, the proposed fixed-time practical DAT algorithms are able to provide an explicit estimation of the upper-bounded convergence time without dependence on initial conditions. The fixed convergence time in the proposed algorithms can be adjusted more flexibly. Finally, some illustrative examples are shown to manifest the validity of the fixed-time practical DAT algorithms.
Bibliografie:ObjectType-Article-1
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2020.2965655