The problem of estimating constant parameters 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.
Bobtsov, A., Yi, B., Ortega, R., Astolfi, A. (2022). Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 67(9), 4746-4753 [10.1109/TAC.2022.3159568].
Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters
Astolfi, A
2022-01-01
Abstract
The problem of estimating constant parameters 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
