A Preview Neuro-Fuzzy Controller Based on Deep Reinforcement Learning for Backing Up a Truck-Trailer Vehicle

Eduardo Bejar, Antonio Moran

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

8 Citas (Scopus)

Resumen

Backing up truck-trailer vehicles is a required task in several industry sectors. Controllers that have been proposed to automate this process usually struggle when the vehicle is required to follow a nonlinear trajectory. This paper presents a neuro-fuzzy controller based on preview control and deep reinforcement learning for reverse parking truck-trailer vehicles. The controller consists of a deep neural network trained with reinforcement learning. A preview control signal is coupled into the trained controller to improve the control performance when tracking complex trajectories. Moreover, a fuzzy logic approach is used to avoid the jackknife state. Simulation results are presented to show that the controller is able to track circular and sinusoidal trajectories.

Idioma originalInglés
Título de la publicación alojada2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728103198
DOI
EstadoPublicada - may. 2019
Evento2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019 - Edmonton, Canadá
Duración: 5 may. 20198 may. 2019

Serie de la publicación

Nombre2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019

Conferencia

Conferencia2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
País/TerritorioCanadá
CiudadEdmonton
Período5/05/198/05/19

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