A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models

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2 Citas (Scopus)
146 Descargas (Pure)

Resumen

In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.
Idioma originalInglés
Título de la publicación alojadaunknown
EditorialIEEE
Páginas1843-1848
Número de páginas6
ISBN (versión digital)978-1-7281-0560-4, 9781728105604
ISBN (versión impresa)978-1-7281-0561-1
DOI
EstadoPublicada - jun 2019
Evento30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, Francia
Duración: 9 jun 201912 jun 2019

Serie de la publicación

Nombre2019-June

Conferencia

Conferencia30th IEEE Intelligent Vehicles Symposium, IV 2019
País/TerritorioFrancia
CiudadParis
Período9/06/1912/06/19

Palabras clave

  • Vehicle Motion
  • Intelligent Vehicles
  • Model Predictive Control

Project and Funding Information

  • Project ID
  • info:eu-repo/grantAgreement/EC/H2020/692455/EU/European Initiative to Enable Validation for Highly Automated Safe and Secure Systems/ENABLE-S3
  • Funding Info
  • This work has been conducted within the ENABLE-S3 project that has_x000D_ received funding from the ECSEL Joint Undertaking under Grant Agreement_x000D_ No 692455. This work was developed at Tecnalia Research & Innovation_x000D_ facilities supporting this research.

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