A Novel Zeroing Neural Dynamics for Real-Time Management of Multi-vehicle Cooperation
Liao, Bolin; Wang, Tinglei; Cao, Xinwei; Hua, Cheng; Li, Shuai (2024-12-23)
IEEE
23.12.2024
B. Liao, T. Wang, X. Cao, C. Hua and S. Li, "A Novel Zeroing Neural Dynamics for Real-Time Management of Multi-vehicle Cooperation," in IEEE Transactions on Intelligent Vehicles, doi: 10.1109/TIV.2024.3519366
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503101919
https://urn.fi/URN:NBN:fi:oulu-202503101919
Tiivistelmä
Abstract
In multi-agent real-time position management tasks, the accuracy of error convergence and convergence time are crucial. This paper reformulates the proposed real-time position management scheme as a quadratic programming problem with equality constraints and solves it in real-time using the zeroing neural dynamics (ZND) model. To enhance the model's ability to detect real-time position management errors, an adaptive parameter finite-time convergent zeroing neural dynamics (AP-FTZND) model is introduced, incorporating adaptive parameters and a nonlinear activation function (AF) within the ZND framework. The global convergence of the AP-FTZND model is proven using the Lyapunov theory, and the upper bound of the convergence time is derived. Finally, the effectiveness and superiority of the AP-FTZND model in solving multi-agent real-time position management tasks are validated through simulations and physical experiments.
In multi-agent real-time position management tasks, the accuracy of error convergence and convergence time are crucial. This paper reformulates the proposed real-time position management scheme as a quadratic programming problem with equality constraints and solves it in real-time using the zeroing neural dynamics (ZND) model. To enhance the model's ability to detect real-time position management errors, an adaptive parameter finite-time convergent zeroing neural dynamics (AP-FTZND) model is introduced, incorporating adaptive parameters and a nonlinear activation function (AF) within the ZND framework. The global convergence of the AP-FTZND model is proven using the Lyapunov theory, and the upper bound of the convergence time is derived. Finally, the effectiveness and superiority of the AP-FTZND model in solving multi-agent real-time position management tasks are validated through simulations and physical experiments.
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