Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles - Université d'Évry Access content directly
Conference Papers Year : 2021

Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles

Abstract

Self-driving cars operate in constantly changing environments and are exposed to a variety of uncertainties and disturbances. These factors render classical controllers ineffective, especially for the lateral control in such vehicles. Therefore, an adaptive MPC controller is designed in this paper for the path tracking task, the developed controller is tuned by an improved particle swarm optimization algorithm. Furthermore, online parameter adaption is performed using Neural Networks and ANFIS. The designed controller showed promising results and adaptation capability against the standard MPC in a triple lane change scenario and a general trajectory test.
Fichier principal
Vignette du fichier
ICNSC2021_YK_NAO_VV_DI_Neural_Network_and_ANFIS_based_autoadaptive_MPC_for_path_tracking_in_autonomous_vehicles.pdf (3.64 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03467943 , version 1 (06-12-2021)

Identifiers

  • HAL Id : hal-03467943 , version 1

Cite

Yassine Kebbati, Naïma Aït Oufroukh, Vincent Vigneron, Dalil Ichalal. Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles. 18ème IEEE International Conference on Networking, Sensing and Control (ICNSC 2021), Dec 2021, Xiamen, China. ⟨hal-03467943⟩
125 View
198 Download

Share

Gmail Facebook X LinkedIn More