UNIVERSITY of GLASGOW

Computing at Glasgow University
 
Paper ID: 6991
DCS Tech Report Number: TR-2003-143

Self-tuning control of non-linear systems using Gaussian process prior models
Sbarbaro,D. Murray-Smith,R.

Publication Type: Tech Report (internal)
Appeared in: DCS Tech Report
Page Numbers :
Publisher: N/A
Year: 2003
Abstract:

Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal(caution) in areas of high uncertainty. As a consequence, the controller has dual features, since it both tracks a reference signal and learns a model of the system from observed responses. The general method and its main features are illustrated on simulation examples.

Keywords: Gaussian process prior, nonparametric statistical model, cautious adaptive control


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