Characterization of Model Uncertainty Features Relevant to Model Predictive Control of Lateral Vehicle Dynamics

EasyChair Preprint 4552

Abstract

The information about a system's dynamics represented by measurement data sets are often confined to regions of restricted operations where the system is not sufficiently excited for model identification purposes. Experiments performed in closed-loop with safety constraints allow only for reduced order modeling. In the paper, a set of low order models are identified from real experimental data of the lateral dynamics of an electric passenger car. Low order models are advantageous for on-line computation in model based control, though uncertainty due to neglected dynamics may deteriorate control performance and constraint satisfaction. The effect of uncertainty is analyzed by controller cross-validation where a controller designed based on one model is evaluated on other models playing the role of the true system. This method allows us to qualify not only model-controller pairs, but to determine the properties of input data and model uncertainty, which lead to more useful data sets, more robust and better performing controllers than the others.

Keyphrases: Classification, Model Predictive Control, Path Tracking Control, autonomous vehicles, feature selection, uncertainty modeling

@booklet{EasyChair:4552,
  author    = {Dániel Pup and Ádám Kisari and Zsombor Vigh and Gábor Rödönyi and Alexandros Soumelidis and József Bokor},
  title     = {Characterization of Model Uncertainty Features Relevant to Model Predictive Control of Lateral Vehicle Dynamics},
  howpublished = {EasyChair Preprint 4552},
  year      = {EasyChair, 2020}}