Model Predictive Control of a Vehicle using Koopman Operator
Abstract
This paper continues in the work from arXiv:1903.06103 [math.OC] 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.
Cite
@article{arxiv.2103.04978,
title = {Model Predictive Control of a Vehicle using Koopman Operator},
author = {Vít Cibulka and Milan Korda and Tomáš Haniš and Martin Hromčík},
journal= {arXiv preprint arXiv:2103.04978},
year = {2021}
}