Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays

Xiongliang Zhang; Shiqi Zheng; Choon Ki Ahn; Yuanlong Xie

doi:10.1109/TNNLS.2022.3146889

Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays

Xiongliang Zhang, Shiqi Zheng, Choon Ki Ahn, Yuanlong Xie

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

Original language	English
Journal	IEEE Transactions on Neural Networks and Learning Systems
DOIs	https://doi.org/10.1109/TNNLS.2022.3146889
Publication status	Accepted/In press - 2022

Keywords

Actuators
Artificial neural networks
Circuit faults
Consensus control
Delays
Fractional-order (FO)
multi-agent systems (MASs)
Nussbaum function
radial basis function neural network (RBF NN)
Robot sensing systems
sensor/actuator faults
time-varying delays.
Time-varying systems

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/TNNLS.2022.3146889

Cite this

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title = "Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays",

abstract = "This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.",

keywords = "Actuators, Artificial neural networks, Circuit faults, Consensus control, Delays, Fractional-order (FO), multi-agent systems (MASs), Nussbaum function, radial basis function neural network (RBF NN), Robot sensing systems, sensor/actuator faults, time-varying delays., Time-varying systems",

author = "Xiongliang Zhang and Shiqi Zheng and Ahn, {Choon Ki} and Yuanlong Xie",

note = "Publisher Copyright: IEEE",

year = "2022",

doi = "10.1109/TNNLS.2022.3146889",

language = "English",

journal = "IEEE Transactions on Neural Networks and Learning Systems",

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AU - Zhang, Xiongliang

AU - Zheng, Shiqi

AU - Ahn, Choon Ki

AU - Xie, Yuanlong

N1 - Publisher Copyright: IEEE

PY - 2022

Y1 - 2022

N2 - This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

AB - This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

KW - Actuators

KW - Artificial neural networks

KW - Circuit faults

KW - Consensus control

KW - Delays

KW - Fractional-order (FO)

KW - multi-agent systems (MASs)

KW - Nussbaum function

KW - radial basis function neural network (RBF NN)

KW - Robot sensing systems

KW - sensor/actuator faults

KW - time-varying delays.

KW - Time-varying systems

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DO - 10.1109/TNNLS.2022.3146889

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SN - 2162-237X

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

ER -

Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays

Abstract

Keywords

ASJC Scopus subject areas

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