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Practical fixed-time adaptive consensus control for a class of multi-agent systems with full state constraints and input delay.

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Title: Practical fixed-time adaptive consensus control for a class of multi-agent systems with full state constraints and input delay.
Authors: Yao, Dajie1,2,3 (AUTHOR), Dou, Chunxia1,3 (AUTHOR) cxdou@ysu.edu.cn, Zhao, Nan3 (AUTHOR), Zhang, Tingjun1,3 (AUTHOR)
Source: Neurocomputing. Jul2021, Vol. 446, p156-164. 9p.
Subjects: Multiagent systems, Adaptive control systems, Radial basis functions, Uncertain systems, Lyapunov functions
Abstract: • The SPFTS scheme is applied to deal with the consensus issue for nonstrict nonlinear MASs. With the help of the proposed strategy, an adaptive NN fixed-time controller is devised to ensure the consensus of MASs. Compared with the finite-time control, the convergence time of the fixed-time consensus control doesn't depend on the initial values. • Full state constrains and input delay are first considered in nonstrict uncertain MASs. By using the backstepping technique, the barrier Lyapunov functions are created to overcome the difficulties caused by full state constraints and the method of Pade approximation is adopted to eliminate the influence of input delay. Then, the output trajectories of followers can track with the signal of the leader in fixed time. • A novel stability analysis method is developed to solve fixed-time consensus problem for for nonlinear nonstrict uncertain MASs in this note. This paper concentrates on a fixed-time consensus issue for nonstrict nonlinear uncertain multi-agent systems (MASs) with state constraints and input delay. In comparison with previous works, the topic of full state constraints and input delay is first embodied in nonstrict MASs in a fixed time. A semi-global practical fixed-time stability (SPFTS) is employed to handle the consensus problem in this note. The radial basis function neural networks (RBFNNs) are developed to counteract unknown items in each agent. Pade approximation approach is introduced to cope with input delay. By using the backstepping technique, adaptive virtual controllers, adaption laws and the actual consensus controller are devised. And the rest followers can converge to a specified trajectory built by the leader in fixed time. Finally, a practical example is employed to test the correctness for the proposed control protocol. [ABSTRACT FROM AUTHOR]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Practical fixed-time adaptive consensus control for a class of multi-agent systems with full state constraints and input delay.
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  Data: <searchLink fieldCode="AR" term="%22Yao%2C+Dajie%22">Yao, Dajie</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dou%2C+Chunxia%22">Dou, Chunxia</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> cxdou@ysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Nan%22">Zhao, Nan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tingjun%22">Zhang, Tingjun</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Jul2021, Vol. 446, p156-164. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertain+systems%22">Uncertain systems</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+functions%22">Lyapunov functions</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • The SPFTS scheme is applied to deal with the consensus issue for nonstrict nonlinear MASs. With the help of the proposed strategy, an adaptive NN fixed-time controller is devised to ensure the consensus of MASs. Compared with the finite-time control, the convergence time of the fixed-time consensus control doesn't depend on the initial values. • Full state constrains and input delay are first considered in nonstrict uncertain MASs. By using the backstepping technique, the barrier Lyapunov functions are created to overcome the difficulties caused by full state constraints and the method of Pade approximation is adopted to eliminate the influence of input delay. Then, the output trajectories of followers can track with the signal of the leader in fixed time. • A novel stability analysis method is developed to solve fixed-time consensus problem for for nonlinear nonstrict uncertain MASs in this note. This paper concentrates on a fixed-time consensus issue for nonstrict nonlinear uncertain multi-agent systems (MASs) with state constraints and input delay. In comparison with previous works, the topic of full state constraints and input delay is first embodied in nonstrict MASs in a fixed time. A semi-global practical fixed-time stability (SPFTS) is employed to handle the consensus problem in this note. The radial basis function neural networks (RBFNNs) are developed to counteract unknown items in each agent. Pade approximation approach is introduced to cope with input delay. By using the backstepping technique, adaptive virtual controllers, adaption laws and the actual consensus controller are devised. And the rest followers can converge to a specified trajectory built by the leader in fixed time. Finally, a practical example is employed to test the correctness for the proposed control protocol. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.neucom.2021.03.032
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 156
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      – SubjectFull: Multiagent systems
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Radial basis functions
        Type: general
      – SubjectFull: Uncertain systems
        Type: general
      – SubjectFull: Lyapunov functions
        Type: general
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      – TitleFull: Practical fixed-time adaptive consensus control for a class of multi-agent systems with full state constraints and input delay.
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            NameFull: Yao, Dajie
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            NameFull: Dou, Chunxia
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            NameFull: Zhao, Nan
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            NameFull: Zhang, Tingjun
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            – D: 25
              M: 07
              Text: Jul2021
              Type: published
              Y: 2021
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