Model Predictive Control of a Vehicle using Koopman Operator. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Cibulka, Vít; Haniš, Tomáš; Korda, Milan; Hromčík, Martin
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 4228
• Page End: 4233
• Publication Date: 2020
• Subjects: Koopman operator -- Eigenfunction -- Eigenvalues -- Basis functions -- Data-driven methods -- Model Predictive Control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2469
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This 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 the linear Koopman operator and used for a linear Model Predictive Control (MPC) design in the high-dimension state space where the nonlinear system dynamics evolve linearly. 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.