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Model-Based Vehicle Dynamics Control for Active Safety

Schofield, Brad LU (2008) In PhD Thesis TFRT-1083
Abstract
The functionality of modern automotive vehicles is becoming increasingly dependent on control systems. Active safety is an area in which control systems play a pivotal role. Currently, rule-based control algorithms are widespread throughout the automotive industry. In order to improve performance and reduce development time, model-based methods may be employed. The primary contribution of this thesis is the development of a vehicle dynamics controller for rollover mitigation.

A central part of this work has been the investigation of control allocation methods, which are used to transform high-level controller commands to actuator inputs in the presence of numerous constraints. Quadratic programming is used to solve a static... (More)
The functionality of modern automotive vehicles is becoming increasingly dependent on control systems. Active safety is an area in which control systems play a pivotal role. Currently, rule-based control algorithms are widespread throughout the automotive industry. In order to improve performance and reduce development time, model-based methods may be employed. The primary contribution of this thesis is the development of a vehicle dynamics controller for rollover mitigation.

A central part of this work has been the investigation of control allocation methods, which are used to transform high-level controller commands to actuator inputs in the presence of numerous constraints. Quadratic programming is used to solve a static optimization problem in each sample. An investigation of the numerical methods used to solve such problems was carried out, leading to the development of a modified active set algorithm.
Vehicle dynamics control systems typically require input from a number of supporting systems, including observers and estimation algorithms. A key parameter for virtually all VDC systems is the friction coefficient. Model-based friction estimation based on the physically-derived brush model is investigated. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor Johansen, Tor Arne, Norwegian University of Science and Technology, Norway
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Control Allocation, Vehicle Dynamics Control, Friction Estimation, Model-Based Control Design
in
PhD Thesis TFRT-1083
pages
186 pages
publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
defense location
Room M:B, M-building, Ole Römers väg 1, Faculty of Engineering, Lund University
defense date
2008-09-19 10:15:00
ISSN
0280-5316
0280-5316
language
English
LU publication?
yes
id
3349cd27-9ed8-483e-8d17-2d0b25f03b1c (old id 1219680)
date added to LUP
2016-04-01 13:35:55
date last changed
2019-05-23 16:00:20
@phdthesis{3349cd27-9ed8-483e-8d17-2d0b25f03b1c,
  abstract     = {{The functionality of modern automotive vehicles is becoming increasingly dependent on control systems. Active safety is an area in which control systems play a pivotal role. Currently, rule-based control algorithms are widespread throughout the automotive industry. In order to improve performance and reduce development time, model-based methods may be employed. The primary contribution of this thesis is the development of a vehicle dynamics controller for rollover mitigation. <br/><br/>A central part of this work has been the investigation of control allocation methods, which are used to transform high-level controller commands to actuator inputs in the presence of numerous constraints. Quadratic programming is used to solve a static optimization problem in each sample. An investigation of the numerical methods used to solve such problems was carried out, leading to the development of a modified active set algorithm.<br/>Vehicle dynamics control systems typically require input from a number of supporting systems, including observers and estimation algorithms. A key parameter for virtually all VDC systems is the friction  coefficient. Model-based friction estimation based on the physically-derived brush model is investigated.}},
  author       = {{Schofield, Brad}},
  issn         = {{0280-5316}},
  keywords     = {{Control Allocation; Vehicle Dynamics Control; Friction Estimation; Model-Based Control Design}},
  language     = {{eng}},
  publisher    = {{Department of Automatic Control, Lund Institute of Technology, Lund University}},
  school       = {{Lund University}},
  series       = {{PhD Thesis TFRT-1083}},
  title        = {{Model-Based Vehicle Dynamics Control for Active Safety}},
  url          = {{https://lup.lub.lu.se/search/files/3470333/1219717.pdf}},
  year         = {{2008}},
}