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Event‐Triggered Distributed Model Predictive Control of Linear Systems With Additive Disturbances.

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Title: Event‐Triggered Distributed Model Predictive Control of Linear Systems With Additive Disturbances.
Authors: Du, Shengli1 (AUTHOR) shenglidu@bjut.edu.cn, Fang, Fang1 (AUTHOR), Cao, Boqi1 (AUTHOR), Wang, Xue‐Fang2 (AUTHOR)
Source: International Journal of Robust & Nonlinear Control. 3/10/2026, Vol. 36 Issue 4, p1581-1593. 13p.
Subjects: Linear systems, Closed loop system stability, Discrete-time systems, Control theory (Engineering), Optimal control theory, Stochastic control theory
Abstract: This article presents an event‐triggered distributed model predictive control (DMPC) framework for discrete‐time linear systems subject to additive bounded disturbances and dynamic couplings. Each subsystem uses a nominal model to formulate a local optimal control problem and employs an error‐based triggering condition that accounts for both its own state prediction error and asynchronously received neighbor predictions. To mitigate additive disturbances, we employ a dual‐mode strategy that applies the MPC law outside the terminal set and switches to a fixed linear feedback law within it to maintain invariance. Explicit conditions that ensure recursive feasibility, closed‐loop stability, and convergence to a disturbance‐invariant set are rigorously derived. Two illustrative case studies demonstrate that the proposed method markedly reduces triggering frequency while preserving control performance under asynchronous information exchange. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Robust & Nonlinear 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 191517285
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Event‐Triggered Distributed Model Predictive Control of Linear Systems With Additive Disturbances.
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  Data: <searchLink fieldCode="AR" term="%22Du%2C+Shengli%22">Du, Shengli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shenglidu@bjut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fang%2C+Fang%22">Fang, Fang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Boqi%22">Cao, Boqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xue‐Fang%22">Wang, Xue‐Fang</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Robust+%26+Nonlinear+Control%22">International Journal of Robust & Nonlinear Control</searchLink>. 3/10/2026, Vol. 36 Issue 4, p1581-1593. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Linear+systems%22">Linear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Closed+loop+system+stability%22">Closed loop system stability</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete-time+systems%22">Discrete-time systems</searchLink><br /><searchLink fieldCode="DE" term="%22Control+theory+%28Engineering%29%22">Control theory (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimal+control+theory%22">Optimal control theory</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+control+theory%22">Stochastic control theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article presents an event‐triggered distributed model predictive control (DMPC) framework for discrete‐time linear systems subject to additive bounded disturbances and dynamic couplings. Each subsystem uses a nominal model to formulate a local optimal control problem and employs an error‐based triggering condition that accounts for both its own state prediction error and asynchronously received neighbor predictions. To mitigate additive disturbances, we employ a dual‐mode strategy that applies the MPC law outside the terminal set and switches to a fixed linear feedback law within it to maintain invariance. Explicit conditions that ensure recursive feasibility, closed‐loop stability, and convergence to a disturbance‐invariant set are rigorously derived. Two illustrative case studies demonstrate that the proposed method markedly reduces triggering frequency while preserving control performance under asynchronous information exchange. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Robust & Nonlinear 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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        Value: 10.1002/rnc.70228
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 1581
    Subjects:
      – SubjectFull: Linear systems
        Type: general
      – SubjectFull: Closed loop system stability
        Type: general
      – SubjectFull: Discrete-time systems
        Type: general
      – SubjectFull: Control theory (Engineering)
        Type: general
      – SubjectFull: Optimal control theory
        Type: general
      – SubjectFull: Stochastic control theory
        Type: general
    Titles:
      – TitleFull: Event‐Triggered Distributed Model Predictive Control of Linear Systems With Additive Disturbances.
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            NameFull: Du, Shengli
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            NameFull: Fang, Fang
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            NameFull: Cao, Boqi
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            NameFull: Wang, Xue‐Fang
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              M: 03
              Text: 3/10/2026
              Type: published
              Y: 2026
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              Value: 36
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            – TitleFull: International Journal of Robust & Nonlinear Control
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