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Cited by 465

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Model Predictive Control for Vehicle Stabilization at the Limits of Handling

Beal, Craig Earl, Gerdes, J. Christian·2013·IEEE Transactions on Control Systems Technology

Abstract

Recent developments in vehicle steering systems offer new opportunities to measure the steering torque and reliably estimate the vehicle sideslip and the tire-road friction coefficient. This paper presents an approach to vehicle stabilization that leverages these estimates to define state boundaries that exclude unstable vehicle dynamics and utilizes a model predictive envelope controller to bound the vehicle motion within this stable region of the state space. This approach provides a large operating region accessible by the driver and smooth interventions at the stability boundaries. Experimental results obtained with a steer-by-wire vehicle and a proof of envelope invariance demonstrate the efficacy of the envelope controller in controlling the vehicle at the limits of handling.

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Crossref · 10.1109/tcst.2012.2200826

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Metadata

Authors
Beal, C. E., & Gerdes, J. C.
Venue
IEEE Transactions on Control Systems Technology
Published
2013-07
Volume
21
Issue
4
Pages
1258-1269
ISSN
  • 1063-6536 (ISSN-L)
  • 1558-0865
OpenAlex
W1979317847
Cited by
465
References
10
FWCI
21.37
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Model Predictive Control for Vehicle Stabilization at the Limits of Handling. https://app.cochat.ai/papers/10.1109/tcst.2012.2200826
The paper
Beal, C. E., & Gerdes, J. C. (2013). Model Predictive Control for Vehicle Stabilization at the Limits of Handling. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2012.2200826

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.