PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Self-tuning control of non-linear systems using Gaussian process prior models
D Sbarbaro and Roderick Murray-Smith
In: Proceedings of the Hamilton Summer School on Switching and Learning in Feedback systems (2005) Springer , Berlin , pp. 140-157.

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 unique features are illustrated on simulation examples.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:1278
Deposited By:Roderick Murray-Smith
Deposited On:28 November 2005