Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

Randa Herzallah*, David Lowe

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Original languageEnglish
Pages (from-to)98-108
Number of pages11
JournalNeural Computing and Applications
Volume12
Issue number2
DOIs
Publication statusPublished - Nov 2003

Bibliographical note

The original publication is available at http://www.springerlink.com/

Keywords

  • distribution modelling
  • error bar
  • neural networks
  • stochastic systems
  • uncertainty

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