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Title: Dynamic adjustment neural network-based cooperative control for vehicle platoons with state constraints

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Group publication title:

AMCS, volume 34 (2024)

Abstract:

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. ; The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle`s state within the platoon. ; By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:86778

DOI:

10.61822/amcs-2024-0015

Pages:

211-224

Source:

AMCS, volume 34, number 2 (2024) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

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