CUED Publications database

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Hall, J and Rasmussen, C and MacIejowski, J (2012) Modelling and control of nonlinear systems using Gaussian processes with partial model information. Proceedings of the IEEE Conference on Decision and Control. pp. 5266-5271. ISSN 0191-2216

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Abstract

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:45
Last Modified: 08 Dec 2014 02:27
DOI: 10.1109/CDC.2012.6426746