Model-Based Vehicle Dynamics Control for Active Safety
(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:
https://lup.lub.lu.se/record/1219680
- author
- Schofield, Brad LU
- supervisor
-
- Tore Hägglund LU
- Anders Rantzer LU
- opponent
-
- Professor Johansen, Tor Arne, Norwegian University of Science and Technology, Norway
- organization
- publishing date
- 2008
- 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}}, }