Recursive Least Squares with Matrix Forgetting

This paper considers an extension of recursive least squares (RLS), where the cost function is modified to include a matrix forgetting factor. Minimization of the modified cost function provides a framework for combined variable-rate and variable-direction (RLS-VRDF) forgetting. This extension of RL...

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Bibliographic Details
Published inProceedings of the American Control Conference pp. 1406 - 1410
Main Authors Bruce, Adam L., Goel, Ankit, Bernstein, Dennis S.
Format Conference Proceeding
LanguageEnglish
Published AACC 01.07.2020
Subjects
Aerospace engineering
Cost function
Covariance matrices
Mathematical model
Signal processing algorithms
Symmetric matrices
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ISSN2378-5861
DOI10.23919/ACC45564.2020.9148005

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Summary:This paper considers an extension of recursive least squares (RLS), where the cost function is modified to include a matrix forgetting factor. Minimization of the modified cost function provides a framework for combined variable-rate and variable-direction (RLS-VRDF) forgetting. This extension of RLS simultaneously addresses two key issues in standard RLS, namely, the need for variable-rate forgetting due to changing plant parameters as well as the need for variable-direction covariance updating due to the loss of persistency. The performance of RSL-VRDF is illustrated by an example with abrupt parameter changes and loss of persistency.
ISSN:2378-5861
DOI:10.23919/ACC45564.2020.9148005