Authors:
Manuel Acosta
;
Stratis Kanarachos
and
Michael E. Fitzpatrick
Affiliation:
Coventry University, United Kingdom
Keyword(s):
Virtual Sensors, Tire Force Estimation, Unscented Kalman Filter, Adaptive Kalman Filter, Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Modeling
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.
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