文章基本信息

标题：TS-MPC for Autonomous Vehicle using a Learning Approach ⁎
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作者：Eugenio Alcala ; Olivier Sename ; Vicenç Puig 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：15110-15115
DOI：10.1016/j.ifacol.2020.12.2034
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, Model Predictive Control (MPC) and Moving Horizon Estimator (MHE) strategies using a data-driven approach to learn a Takagi-Sugeno (TS) representation of the vehicle dynamics are proposed to solve autonomous driving control problems in real-time. To address the TS modeling, we use the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to obtain a set of polytopic-based linear representations as well as a set of membership functions relating in a non-linear way the different linear subsystems. The proposed control approach is provided by racing-based references of an external planner and estimations from the MHE offering a high driving performance in racing mode. The control-estimation scheme is tested in a simulated racing environment to show the potential of the proposed approaches.
关键词：KeywordsTakagi-Sugeno approachModel predictive controlAutonomous vehiclesData-driven identificationLearning control