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Distributed incremental model predictive control for safe formation control of multiagent systems.

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Title: Distributed incremental model predictive control for safe formation control of multiagent systems.
Authors: He, Defeng1 (AUTHOR) hdfzj@zjut.edu.cn, Zhao, Xiaoduo1 (AUTHOR), Mu, Jianbin1 (AUTHOR)
Source: Asian Journal of Control. May2026, Vol. 28 Issue 3, p1275-1286. 12p.
Subjects: Multiagent systems, Predictive control systems, Mathematical optimization, Feedback control systems, Obstacle avoidance (Robotics), Decentralized control systems, Dynamic stability
Abstract: This paper proposes a distributed incremental model predictive control (DIMPC) algorithm for safe and efficient formation control of constrained multiagent systems. The formation problem is firstly formulated in the virtual structure framework. Then an incremental parameterization is elegantly designed to reduce the computational burden of the optimization problem of MPC. To guarantee the safety of each agent in the formation, an on‐demand collision avoidance/obstacle strategy is presented using the predictability of MPC. Moreover, the weighted norm of the predicted state increments is added to the cost function to improve control performance and some linear matrix inequalities are derived to guarantee the stability of the multiagent with DIMPC. Comparative simulations verify the reliability and advantages of the proposed algorithm in terms of online computational efficiency and control performance. [ABSTRACT FROM AUTHOR]
Copyright of Asian Journal of Control is the property of Wiley-Blackwell 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.)
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  Data: Distributed incremental model predictive control for safe formation control of multiagent systems.
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  Data: <searchLink fieldCode="AR" term="%22He%2C+Defeng%22">He, Defeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hdfzj@zjut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xiaoduo%22">Zhao, Xiaoduo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mu%2C+Jianbin%22">Mu, Jianbin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. May2026, Vol. 28 Issue 3, p1275-1286. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Obstacle+avoidance+%28Robotics%29%22">Obstacle avoidance (Robotics)</searchLink><br /><searchLink fieldCode="DE" term="%22Decentralized+control+systems%22">Decentralized control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+stability%22">Dynamic stability</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper proposes a distributed incremental model predictive control (DIMPC) algorithm for safe and efficient formation control of constrained multiagent systems. The formation problem is firstly formulated in the virtual structure framework. Then an incremental parameterization is elegantly designed to reduce the computational burden of the optimization problem of MPC. To guarantee the safety of each agent in the formation, an on‐demand collision avoidance/obstacle strategy is presented using the predictability of MPC. Moreover, the weighted norm of the predicted state increments is added to the cost function to improve control performance and some linear matrix inequalities are derived to guarantee the stability of the multiagent with DIMPC. Comparative simulations verify the reliability and advantages of the proposed algorithm in terms of online computational efficiency and control performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Asian Journal of Control is the property of Wiley-Blackwell 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:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/asjc.3725
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1275
    Subjects:
      – SubjectFull: Multiagent systems
        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Feedback control systems
        Type: general
      – SubjectFull: Obstacle avoidance (Robotics)
        Type: general
      – SubjectFull: Decentralized control systems
        Type: general
      – SubjectFull: Dynamic stability
        Type: general
    Titles:
      – TitleFull: Distributed incremental model predictive control for safe formation control of multiagent systems.
        Type: main
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          Name:
            NameFull: He, Defeng
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          Name:
            NameFull: Zhao, Xiaoduo
      – PersonEntity:
          Name:
            NameFull: Mu, Jianbin
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      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 15618625
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            – Type: volume
              Value: 28
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Asian Journal of Control
              Type: main
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