# Persistent Excitation is Unnecessary for On-line Exponential Parameter Estimation: A New Algorithm that Overcomes this Obstacle

@article{Korotina2021PersistentEI, title={Persistent Excitation is Unnecessary for On-line Exponential Parameter Estimation: A New Algorithm that Overcomes this Obstacle}, author={M. Korotina and J. G. Romero and S. Aranovskiy and A. Bobtsov and R. Ortega}, journal={ArXiv}, year={2021}, volume={abs/2106.08773} }

In this paper we prove that it is possible to estimate on-line the parameters of a classical vector linear regression equation Y = Ωθ, where Y ∈ R, Ω ∈ R are bounded, measurable signals and θ ∈ R 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… Expand

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Identifiability Implies Robust, Globally Exponentially Convergent On-line Parameter Estimation: Application to Model Reference Adaptive Control

- Computer Science, Engineering
- ArXiv
- 2021

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression… Expand

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