Event-triggered Model Predictive Control for Vehicle Platoons Subject to Multiple Constraints

The paper investigates the event-triggered dual model predictive control for vehicle platoons subject to multiple constraints. First of all, a dynamics model is constructed for the vehicle platoon system. After that, a model predictive control algorithm is proposed to ensure that the vehicle platoon satisfies the multi-constraint problem. Then, an event-triggered mechanism is designed to reduce the consumption of computational resources. Next, the developed algorithm is analyzed to show that it satisfies the constrained optimization problem. Finally, a simulation is carried out to verify the effectiveness of the algorithm.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/icmee63700.2024.11025332
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Event-triggered Model Predictive Control for Vehicle Platoons Subject to Multiple Constraints

Panpan Yang, Dipak Sen, Yuan Qian, Huang Congxian
Traffic control and management
article

Event-triggered Model Predictive Control for Vehicle Platoons Subject to Multiple Constraints

Panpan Yang, Dipak Sen, Yuan Qian, Huang Congxian
article en

Abstract

The paper investigates the event-triggered dual model predictive control for vehicle platoons subject to multiple constraints. First of all, a dynamics model is constructed for the vehicle platoon system. After that, a model predictive control algorithm is proposed to ensure that the vehicle platoon satisfies the multi-constraint problem. Then, an event-triggered mechanism is designed to reduce the consumption of computational resources. Next, the developed algorithm is analyzed to show that it satisfies the constrained optimization problem. Finally, a simulation is carried out to verify the effectiveness of the algorithm.

Chang'an University (CN)
National Natural Science Foundation of China, Research and Development
Climate action
Openalex Percentile: Top 62%
Traffic control and management
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