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
