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<p>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.</p>
</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.

Dynamic Adjustment Neural Network–Based Cooperative Control for Vehicle Platoons with State Constraints

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are...

Dynamic Adjustment Neural Network–Based Cooperative Control for Vehicle Platoons with State Constraints

Publicado en línea: 25 jun 2024

Páginas: 211 - 224

Recibido: 06 nov 2023

Aceptado: 10 mar 2024

DOI: https://doi.org/10.61822/amcs-2024-0015

Palabras clave
vehicle platoon, dynamic adjustment neural network (DANN), cooperative control, state constraint

© 2024 Ping Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Dynamic Adjustment Neural Network–Based Cooperative Control for Vehicle Platoons with State Constraints

Publicado en línea: 25 jun 2024

Páginas: 211 - 224

Recibido: 06 nov 2023

Aceptado: 10 mar 2024

DOI: https://doi.org/10.61822/amcs-2024-0015

Palabras clavevehicle platoon, dynamic adjustment neural network (DANN), cooperative control, state constraint

© 2024 Ping Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
vehicle platoon, dynamic adjustment neural network (DANN), cooperative control, state constraint