Nonlinear adaptive control using non-parametric Gaussian Process prior models

Murray-Smith, R. and Sbarbaro, D. (2002) Nonlinear adaptive control using non-parametric Gaussian Process prior models. In: 15th Triennial World Congress of the International Federation of Automatic Control, Barcelona, Spain, 21-26 July 2002,

Abstract

Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used 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), and excitation of the system. The controller has dual features, since it is both tracking a reference signal and learning a model of the system from observed responses. The general method and its main features are illustrated on a simulation example.

• Item Type: Conference Proceedings
• Keywords: Gaussian process priors, nonparametric models, dual control, nonlinear model-based predictive control
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick
• Authors: Murray-Smith, R., and Sbarbaro, D.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science; T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: International Federation of Automatic Control (IFAC)
• Copyright Holders: Copyright © 2002 Elsevier
• First Published: First published in Barcelona
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher