A new on-line exponential parameter estimator without persistent excitation
| Autor: | Romeo Ortega, Alexey A. Bobtsov, Marina Korotina, Jose Guadalupe Romero, Stanislav Aranovskiy |
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| Přispěvatelé: | Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec, Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), 18-19-00627, Russian Science Foundation |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Estimation theory Mechanical Engineering 020208 electrical & electronic engineering Dynamic regressor extending and mixing Estimator 02 engineering and technology Exponential function [SPI.AUTO]Engineering Sciences [physics]/Automatic 020901 industrial engineering & automation Nonlinear filter Control and Systems Engineering Bounded function Convergence (routing) Linear regression Line (geometry) 0202 electrical engineering electronic engineering information engineering Parameter estimation Applied mathematics Electrical and Electronic Engineering Interval excitation Constant (mathematics) Mathematics Persistent excitation |
| Zdroj: | Systems and Control Letters Systems and Control Letters, Elsevier, 2022, 159, pp.105079. ⟨10.1016/j.sysconle.2021.105079⟩ Systems and Control Letters, 2022, 159, pp.105079. ⟨10.1016/j.sysconle.2021.105079⟩ |
| ISSN: | 0167-6911 1872-7956 |
| Popis: | In this paper we propose a new algorithm that estimates on-line the parameters of a classical vector linear regression equation Y = Ω θ , where Y ∈ R n , Ω ∈ R n × q are bounded, measurable signals and θ ∈ R q is a constant vector of unknown parameters, even when the regressor Ω is not persistently exciting. Moreover, the convergence of the new parameter estimator is global and exponential and is given for both, continuous-time and discrete-time implementations. As an illustration example we consider the problem of parameter estimation of a linear time-invariant system, when the input signal is not sufficiently exciting, which is known to be a necessary and sufficient condition for the solution of the problem with standard gradient or least-squares adaptation algorithms. |
| Databáze: | OpenAIRE |
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