Improved Transients in Multiple Frequencies Estimation via Dynamic Regressor Extension and Mixing

Autor: Stanislav, Aranovskiy, Alexey, Bobtsov, Romeo, Ortega, Anton, Pyrkin

A problem of performance enhancement for multiple frequencies estimation is studied. First, we consider a basic gradient-based estimation approach with global exponential convergence. Next, we apply dynamic regressor extension and mixing technique to

Externí odkaz: http://arxiv.org/abs/1604.01928

A Parameter Estimation Approach to State Observation of Nonlinear Systems

Autor: Romeo, Ortega, Alexey, Bobtsov, Anton, Pyrkin, Stanislav, Aranovskiy

Publikováno v: Systems & Control Letters, Volume 85, November 2015, Pages 84-94

A novel approach to the problem of partial state estimation of nonlinear systems is proposed. The main idea is to translate the state estimation problem into one of estimation of constant, unknown parameters related to the systems initial conditions.

Externí odkaz: http://arxiv.org/abs/1508.03959

Bias Propagation and Estimation in Homogeneous Differentiators for a Class of Mechanical Systems

Autor: Stanislav Aranovskiy, Igor Ryadchikov, Evgeny Nikulchev, Jian Wang, Dmitry Sokolov

Publikováno v: IEEE Access, Vol 8, Pp 19450-19459 (2020)

Motivated by non-anthropomorphic dynamic stabilization of a walking robot, we consider the bias propagation problem for a homogeneous nonlinear model-based differentiator applied to a reaction wheel pendulum with a biased position sensor. We show tha

Externí odkaz: https://doaj.org/article/07575cc39a8d4a5fbe36268648ada21d

A new least squares parameter estimator for nonlinear regression equations with relaxed excitation conditions and forgetting factor

Autor: Romeo Ortega, Jose Guadalupe Romero, Stanislav Aranovskiy

Publikováno v: Systems and Control Letters
Systems and Control Letters, 2022, 169, pp.105377. ⟨10.1016/j.sysconle.2022.105377⟩

International audience; In this note a new high performance least squares parameter estimator is proposed. The main features of the estimator are: (i) global exponential convergence is guaranteed for all identifiable linear regression equations; (ii)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9ab09300cd2c16bae2ed4a2eedd653e
https://hal-centralesupelec.archives-ouvertes.fr/hal-03793150/file/ortega_scl22_submitted.pdf

A new on-line exponential parameter estimator without persistent excitation

Autor: Romeo Ortega, Alexey A. Bobtsov, Marina Korotina, Jose Guadalupe Romero, Stanislav Aranovskiy

Publikováno v: 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⟩

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 unkno

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1fa900809ad13a1c538156cf38e85d6
https://hal-centralesupelec.archives-ouvertes.fr/hal-03463648/file/mainSCL_accepted.pdf