Abstract
In this paper, a novel approach to estimate the longitudinal, lateral and vertical tire forces is presented. The innovation lies a) in the proposition of a modular state estimation architecture that lessens the tuning effort and ensures the filter’s stability and b) in the estimation of the longitudinal velocity relying only on the wheel speed information.The longitudinal forces are estimated using an Adaptive Random-Walk Linear Kalman Filter. The lateral forces per axle are estimated by combining an Adaptive Unscented Kalman filter and Neural Networks. The individual tire lateral forces are inferred from the axle lateral forces using the vertical load proportionality principle. The individual tire vertical forces are estimated using a steady-state weight transfer approach, in which the roll stiffness distribution is considered. The state estimator is implemented in Simulink R and simulations are carried out in the vehicle dynamics simulation software IPG CarMaker R . The virtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance.
Original language | English |
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Title of host publication | ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics |
Publisher | SciTePress |
Pages | 386-397 |
Number of pages | 12 |
Volume | 1 |
ISBN (Electronic) | 9789897582639 |
DOIs | |
Publication status | Published - 26 Jul 2017 |
Event | 14th International Conference on Informatics in Control, Automation and Robotics - Madrid, Spain Duration: 26 Jul 2017 → 28 Jul 2017 http://www.ieee-ras.org/component/rseventspro/event/1040-icinco-2017-international-conference-on-informatics-in-control-automation-and-robotics |
Conference
Conference | 14th International Conference on Informatics in Control, Automation and Robotics |
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Abbreviated title | ICINCO 2017 |
Country | Spain |
City | Madrid |
Period | 26/07/17 → 28/07/17 |
Internet address |
Keywords
- Virtual Sensors
- Neural Networks
- Adaptive Kalman Filter
- Unscented Kalman Filter
- Tire force estimation
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Control and Systems Engineering