onparametric Identification of Linearizations and Uncertainty using Gaussian Process Models - Application to Robust Wheel Slip Control
J. Hansen, Roderick Murray-Smith and T. A. Johansen
In: Joint 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC'05), Seville. Spain(2005).
Abstract—Gaussian process prior models offer a nonparametric
approach to modelling unknown nonlinear systems from
experimental data. These are flexible models which automatically
adapt their model complexity to the available data, and
which give not only mean predictions but also the variance
of these predictions. A further advantage is the analytical
derivation of derivatives of the model with respect to inputs,
with their variance, providing a direct estimate of the locally
linearized model with its corresponding parameter variance.
We show how this can be used to tune a controller based on
the linearized models, taking into account their uncertainty.
The approach is applied to a simulated wheel slip control task
illustrating controller development based on a nonparametric
model of the unknown friction nonlinearity. Local stability and
robustness of the controllers are tuned based on the uncertainty
of the nonlinear models’ derivatives.