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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]
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Database: Engineering Source
Description
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]
ISSN:09521976
DOI:10.1016/j.engappai.2026.114743