Corpus ID: 235446589

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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