Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise

A. Anil Meera, M. Wisse

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
29 Downloads (Pure)

Abstract

The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we introduce a novel online noise smoothness estimator based on the idea of free energy principle. We mathematically show that our estimator can converge to the free energy optimum during smoothness estimation. Using this formulation, we introduce a joint state and noise smoothness observer design called DEMs. Through rigorous simulations, we show that DEMs outperforms state-of-the-art state observers with least state estimation error. Finally, we provide a proof of concept for DEMs by applying it on a real life robotics problem - state estimation of a quadrotor hovering in wind, demonstrating its practical use.
Original languageEnglish
Title of host publicationProceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)
PublisherIEEE
Pages1888-1893
ISBN (Print)978-1-6654-6761-2
DOIs
Publication statusPublished - 2022
EventIEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico
Duration: 6 Dec 20229 Dec 2022

Conference

ConferenceIEEE 61st Conference on Decision and Control (CDC 2022)
Country/TerritoryMexico
CityCancún
Period6/12/229/12/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Linear systems
  • Neuroscience
  • Observers
  • Stability analysis
  • Mathematical models
  • Colored noise
  • Task analysis

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