文章基本信息

标题：Model Predictive Control of a Vehicle using Koopman Operator
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作者：Vít Cibulka ; Tomáš Haniš ; Milan Korda 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：4228-4233
DOI：10.1016/j.ifacol.2020.12.2469
语种：English
出版社：Elsevier
摘要：AbstractThis paper continues in the work from Cibulka et al. (2019) where a nonlinear vehicle model was approximated in a purely data-driven manner by a linear predictor of higher order, namely the Koopman operator. The vehicle system typically features a lot of nonlinearities such as rigid-body dynamics, coordinate system transformations and most importantly the tire. These nonlinearities are approximated in a predefined subset of the state-space by thelinearKoopman operator and used for alinearModel Predictive Control (MPC) design in the high-dimension state space where the nonlinear system dynamics evolvelinearly.The result is a nonlinear MPC designed by linear methodologies. It is demonstrated that the Koopman-based controller is able to recover from a very unusual state of the vehicle where all the aforementioned nonlinearities are dominant. The controller is compared with a controller based on a classic local linearization and shortcomings of this approach are discussed.
关键词：KeywordsKoopman operatorEigenfunctionEigenvaluesBasis functionsData-driven methodsModel Predictive Control