Adaptive, cautious, predictive control with Gaussian process priors

Murray-Smith, R., Sbarbaro, D., Rasmussen, C.E. and Girard, A. (2003) Adaptive, cautious, predictive control with Gaussian process priors. In: 13th IFAC Symposium on System Identification, Rotterdam, Netherlands, 27-29 August 2003, pp. 1195-1200. ISBN 0080437095

Abstract

Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

• Item Type: Conference Proceedings
• Keywords: cautious control, Gaussian process priors, nonparametric models, nonlinear model-based predictive control, propagation of uncertainty
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick
• Authors: Murray-Smith, R., Sbarbaro, D., Rasmussen, C.E., and Girard, A.
• Subjects: T Technology > TJ Mechanical engineering and machinery
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Elsevier Science
• ISBN: 0080437095
• Copyright Holders: Copyright © 2003 Elsevier Science
• First Published: First published in England
• Publisher Policy: Reproduced with the permission of the publisher