PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Bayesian learning of nonlinear hidden state-space models for model predictive control
Tapani Raiko and Matti Tornio
Neurocomputing Volume 72, Number 16-18, pp. 3704-3712, 2009. ISSN 0925-2312

Abstract

This paper studies the identification and model predictive control in nonlinear hidden state-space models. Nonlinearities are modelled with neural networks and system identification is done with variational Bayesian learning. In addition to the robustness of control, the stochastic approach allows for various control schemes, including combinations of direct and indirect controls, as well as using probabilistic inference for control. We study the noise-robustness, speed, and accuracy of three different control schemes as well as the effect of changing horizon lengths and initialisation methods using a simulated cart–pole system. The simulations indicate that the proposed method is able to find a representation of the system state that makes control easier especially under high noise.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6398
Deposited By:Tapani Raiko
Deposited On:08 March 2010