Yaw Rate and Lateral Acceleration Sensor Plausibilisation in an Active Front Steering Vehicle
Accurate measurements from sensors measuring the vehicle?s lateral
behavior are vital in todays vehicle dynamic control systems such as
the Electronic Stability Program (ESP). This thesis concerns accurate
plausibilisation of two of these sensors, namely the yaw rate sensor
and the lateral acceleration sensor. The estimation is based on Kalman
filtering and culminates in the use of a 2 degree-of-freedom nonlinear
two-track model describing the vehicle lateral dynamics. The unknown
and time-varying cornering stiffnesses are adapted and other unknown
quantities such as yaw moment of inertia is estimated. The Kalman
filter transforms the measured signals into a sequence of residuals
that are then investigated with the aid of various change detection
methods such as the CuSum algorithm. An investigation into the area of
adaptive thresholding has also been made.
The change detection methods investigated successfully detects faults
in both the yaw rate and the lateral acceleration sensor. It it also
shown that adaptive thresholding can be used to improve the diagnosis
system. All of the results have been evaluated on-line in a prototype
vehicle with real-time fault injection.
Anders Wikström
2006
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Last updated: 2021-11-10
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