A Method for Handling Uncertainty in Evolutionary Optimization with an Application to Feedback Control of Combustion

Nikolaus Hansen 1, 2, * Andre Niederberger 3 Lino Guzzella 3 Petros Koumoutsakos 4, 5
* Corresponding author
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : We present a novel method for handling uncertainty in evolutionary optimization. The method entails quantification and treatment of uncertainty and relies on the rank based selection operator of evolutionary algorithms. The proposed uncertainty handling is implemented in the context of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and verified on test functions. The present method is independent of the uncertainty distribution, prevents premature convergence of the evolution strategy and is well suited for online optimization as it requires only a small number of additional function evaluations. The algorithm is applied in an experimental set-up to the online optimization of feedback controllers of thermoacoustic instabilities of gas turbine combustors. In order to mitigate these instabilities, gain-delay or model-based Hinfty controllers sense the pressure and command secondary fuel injectors. The parameters of these controllers are usually specified via a trial and error procedure. We demonstrate that their online optimization with the proposed methodology enhances, in an automated fashion, the online performance of the controllers, even under highly unsteady operating conditions, and it also compensates for uncertainties in the model-building and design process.
Document type :
Journal articles
Complete list of metadatas

Cited literature [55 references]  Display  Hide  Download

https://hal.inria.fr/inria-00276216
Contributor : Nikolaus Hansen <>
Submitted on : Saturday, June 21, 2008 - 4:17:43 PM
Last modification on : Sunday, July 21, 2019 - 1:48:11 AM
Long-term archiving on : Friday, May 28, 2010 - 5:57:27 PM

File

TEC2008.pdf
Publisher files allowed on an open archive

Identifiers

  • HAL Id : inria-00276216, version 1

Collections

Citation

Nikolaus Hansen, Andre Niederberger, Lino Guzzella, Petros Koumoutsakos. A Method for Handling Uncertainty in Evolutionary Optimization with an Application to Feedback Control of Combustion. IEEE Transactions on Evolutionary Computation, Institute of Electrical and Electronics Engineers, 2009. ⟨inria-00276216⟩

Share

Metrics

Record views

533

Files downloads

2115