Estimation based on acceleration measures of an active suspension plant
The vehicle suspension system is responsible for comfort, safety and vehicle control. In order to positively manipulate these properties, control and estimation theory are used to adapt the system to different road conditions. This paper considers three estimation methods, which are designed to retr...
- Autores:
- Tipo de recurso:
- masterThesis
- Fecha de publicación:
- 2015
- Institución:
- Pontificia Universidad Javeriana
- Repositorio:
- Repositorio Universidad Javeriana
- Idioma:
- spa
- OAI Identifier:
- oai:repository.javeriana.edu.co:10554/19608
- Palabra clave:
- Kalman filter
Particle filter
Neural network
Active suspension
Maestría en ingeniería electrónica - Tesis y disertaciones académicas
Filtración Kalman
Partículas
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| Summary: | The vehicle suspension system is responsible for comfort, safety and vehicle control. In order to positively manipulate these properties, control and estimation theory are used to adapt the system to different road conditions. This paper considers three estimation methods, which are designed to retrieve the system states using only acceleration measures: the Kalman Filter, Particle Filter and Artificial Neuronal Network. Also it considers three control methods: LQR and pole location which it minimizes, the chassis acceleration (a variable used to improve the vehicle comfort). Finally the controllers and estimators are implemented in simulation and in the real plant, using the model of the Quanser active suspension plant. |
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