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Self-tuning control of non-linear systems using gaussian process prior models

By D. Sbarbaro and R. Murray-Smith


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

Topics: QA75
Publisher: Springer
Year: 2005
OAI identifier:
Provided by: Enlighten

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