ニューラルネットワークとANFISによる自動運転車の経路追従向け自己適応型MPC
Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles
自動運転車の経路追従のための適応型MPCを提案し、ニューラルネットワークとANFISでオンラインにパラメータを調整することで、従来のMPCより優れた性能を示した。
著者: Yassine Kebbati, Naima Ait-Oufroukh, Vincent Vigneron, Dalil Ichala
分類: cs.RO, math.OC
原文アブストラクト
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 lateral control. Therefore, an adaptive MPC controller is designed in this paper for the path tracking task, tuned by an improved particle swarm optimization algorithm. Online parameter adaptation is performed using Neural Networks and ANFIS. The designed controller showed promising results compared to standard MPC in triple lane change and trajectory tracking scenarios. Code can be found here: https://github.com/yassinekebbati/NN_MPC-vs-ANFIS_MPC