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arXiv · 2104.02210

Generation of new exciting regressors for consistent on-line estimation of unknown constant parameters

Alexey Bobtsov·Bowen Yi·Romeo Ortega·Alessandro Astolfi
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Abstract

The problem of parameter estimation from a standard vector linear regression equation in the absence of sufficient excitation in the regressor is addressed. The first step to solve the problem consists in transforming this equation into a set of scalar ones using the well-known dynamic regressor extension and mixing technique. Then a novel procedure to generate new scalar exciting regressors is proposed.} The superior performance of a classical gradient estimator using this new regressor, instead of the original one, is illustrated with comprehensive simulations.

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Alexey Bobtsov, Bowen Yi, Romeo Ortega, Alessandro Astolfi. 2021-04-06. Generation of new exciting regressors for consistent on-line estimation of unknown constant parameters. https://arxiv.org/abs/2104.02210

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