Distribution modeling of nonlinear inverse controllers under a Bayesian framework

Randa Herzallah*, David Lowe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems and is demonstrated on nonlinear single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) examples.

Original languageEnglish
Pages (from-to)107-114
Number of pages8
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2007

Keywords

  • distribution modelling
  • inverse controller
  • neural networks
  • stochastic systems
  • uncertainty

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