2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018
DOI: 10.1109/itsc.2018.8569751
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Importance of Vertical Dynamics for Accurate Modelling, Friction Estimation and Vehicle Motion Control

Abstract: Autonomous vehicles are round the corner. Most of car manufacturers are racing to become a leader in this promising market. In this paper, we focus on one of the remaining challenges to be overcome, namely, the control of vehicle motion when friction varies. Researchers tend to estimate the friction coefficient using complex observers based on simple mathematical equation. We believe that by taken into account some formerly simplified dynamics, as roll dynamics, we can reduce the estimations' errors, and there… Show more

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Cited by 3 publications
(4 citation statements)
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“…This longitudinal stiffness depends on varying-parameters α, μ, and F z . These parameters could be estimated online (Kissai et al, 2018). It depends also on constant parameters C s , C α , and κ * .…”
Section: Longitudinal Force Linearizationmentioning
confidence: 99%
“…This longitudinal stiffness depends on varying-parameters α, μ, and F z . These parameters could be estimated online (Kissai et al, 2018). It depends also on constant parameters C s , C α , and κ * .…”
Section: Longitudinal Force Linearizationmentioning
confidence: 99%
“…We suppose that the friction coefficient and the vertical loads can be estimated using for example the methods detailed in [27]. The desired acceleration v contains in this case the global yaw moment M ztot .…”
Section: Control Allocationmentioning
confidence: 99%
“…RESULTS Two major features should be verified in this section: control robustness and control allocation benefits. Regarding control robustness, we compare controllers developed and validated by co-simulation using a high-fidelity software as in [9], [12], [27], and the controllers developed in this paper using an experimental maneuver. However, to show the benefits of control allocation algorithms, we only use simulation of the validated identified models by experiment.…”
Section: Control Allocationmentioning
confidence: 99%
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