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Distributed Lyapunov-based model predictive formation control for unmanned surface vehicles with flexible-time prescribed performance.

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Title: Distributed Lyapunov-based model predictive formation control for unmanned surface vehicles with flexible-time prescribed performance.
Authors: Yan, Zengyang1 (AUTHOR) zengyangyan@hainanu.edu.cn, Wu, Di1,2 (AUTHOR) hainuwudi@hainanu.edu.cn, Qiao, Lei3 (AUTHOR) qiaolei@sjtu.edu.cn, Du, Baozhu1 (AUTHOR) baozhudu@hainanu.edu.cn, Zhang, Guoqing4 (AUTHOR) zgq_dlmu@163.com, Lippiello, Vincenzo5 (AUTHOR) vincenzo.lippiello@unina.it
Source: Engineering Applications of Artificial Intelligence. Jul2026:Part 1, Vol. 176, pN.PAG-N.PAG. 1p.
Subjects: Predictive control systems, Cooperative control systems, Autonomous vehicles, Control theory (Engineering), Prediction models, Decentralized control systems
Abstract: This paper investigates the cooperative maneuvering control of unmanned surface vehicle (USV) formations in complex and time-varying marine environments subject to nonlinear disturbances and input constraints. To address the coupling between formation evolution and velocity regulation, a path-parameterized formation model is established to decouple the spatial and temporal dynamics, thereby enabling coordinated control of position and speed while preserving geometric consistency. To improve transient and steady-state performance, a flexible-time prescribed performance (FTPP) mechanism is developed to construct asymmetric and time-varying performance bounds. Different from conventional prescribed performance designs, the transformed error generated by FTPP is further embedded into the contraction constraint of the distributed Lyapunov-based model predictive control (DLMPC) framework. This coupling enables the predictive controller to inherit the convergence characteristics of FTPP and the stability of the auxiliary controller while optimizing constrained control inputs, thereby improving tracking accuracy, accelerating convergence, and reducing overshoot. To enhance disturbance estimation capability, a feature-enhanced neural predictor (FENP) is integrated into the DLMPC framework. In contrast to conventional radial basis function neural predictors, the proposed FENP introduces a Hadamard-product-based feature enhancement mechanism to enrich the neural-network input representation and improve the approximation of complex nonlinear uncertainties. Simulation results demonstrate that the proposed DLMPC–FTPP–FENP framework achieves accurate formation maneuvering, fast convergence, and robust closed-loop performance under complex marine disturbances. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science 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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Header DbId: egs
DbLabel: Engineering Source
An: 193498774
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Distributed Lyapunov-based model predictive formation control for unmanned surface vehicles with flexible-time prescribed performance.
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  Data: <searchLink fieldCode="AR" term="%22Yan%2C+Zengyang%22">Yan, Zengyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zengyangyan@hainanu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Di%22">Wu, Di</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hainuwudi@hainanu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Qiao%2C+Lei%22">Qiao, Lei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> qiaolei@sjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Du%2C+Baozhu%22">Du, Baozhu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> baozhudu@hainanu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Guoqing%22">Zhang, Guoqing</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> zgq_dlmu@163.com</i><br /><searchLink fieldCode="AR" term="%22Lippiello%2C+Vincenzo%22">Lippiello, Vincenzo</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> vincenzo.lippiello@unina.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Jul2026:Part 1, Vol. 176, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+control+systems%22">Cooperative control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Control+theory+%28Engineering%29%22">Control theory (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Decentralized+control+systems%22">Decentralized control systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper investigates the cooperative maneuvering control of unmanned surface vehicle (USV) formations in complex and time-varying marine environments subject to nonlinear disturbances and input constraints. To address the coupling between formation evolution and velocity regulation, a path-parameterized formation model is established to decouple the spatial and temporal dynamics, thereby enabling coordinated control of position and speed while preserving geometric consistency. To improve transient and steady-state performance, a flexible-time prescribed performance (FTPP) mechanism is developed to construct asymmetric and time-varying performance bounds. Different from conventional prescribed performance designs, the transformed error generated by FTPP is further embedded into the contraction constraint of the distributed Lyapunov-based model predictive control (DLMPC) framework. This coupling enables the predictive controller to inherit the convergence characteristics of FTPP and the stability of the auxiliary controller while optimizing constrained control inputs, thereby improving tracking accuracy, accelerating convergence, and reducing overshoot. To enhance disturbance estimation capability, a feature-enhanced neural predictor (FENP) is integrated into the DLMPC framework. In contrast to conventional radial basis function neural predictors, the proposed FENP introduces a Hadamard-product-based feature enhancement mechanism to enrich the neural-network input representation and improve the approximation of complex nonlinear uncertainties. Simulation results demonstrate that the proposed DLMPC–FTPP–FENP framework achieves accurate formation maneuvering, fast convergence, and robust closed-loop performance under complex marine disturbances. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science 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.engappai.2026.114743
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Cooperative control systems
        Type: general
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Control theory (Engineering)
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Decentralized control systems
        Type: general
    Titles:
      – TitleFull: Distributed Lyapunov-based model predictive formation control for unmanned surface vehicles with flexible-time prescribed performance.
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            NameFull: Yan, Zengyang
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              M: 07
              Text: Jul2026:Part 1
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              Y: 2026
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              Value: 176
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