no code implementations • 21 Feb 2024 • Olga Kozachek, Nikolay Nikolaev, Olga Slita, Alexey Bobtsov
In this paper an adaptive state observer and parameter identification algorithm for a linear time-varying system are developed under condition that the state matrix of the system contains unknown time-varying parameters of a known form.
no code implementations • 12 Aug 2023 • Carlo Beltran, Alexey Bobtsov, Romeo Ortega, Diego Langarica-Cordoba, Rafael Cisneros, Luis H. Diaz-Saldierna
In this paper, we address the problem of online parameter estimation of a Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve, that is the static relation between the voltage and the current of the PEMFC.
no code implementations • 25 May 2023 • Vladimir Vorobyev, Alexey Bobtsov, Nikolay Nikolaev, Anton Pyrkin
The article investigates an algorithm for identifying an unknown constant parameter for a scalar regression model using a nonlinear operator that allows us to obtain a new regression equation (with an expanded number of unknown parameters) for which the influence of interference in measurement or disturbance will be minimal.
no code implementations • 24 May 2023 • Olga Kozachek, Alexey Bobtsov, Nikolay Nikolaev
The paper proposes an adaptive observer of the state vector of a nonlinear time varying system based on measurements of the output variable.
no code implementations • 24 May 2023 • Alexey Bobtsov, Vladimir Virobyev, Nikolay Nikolaev, Anton Pyrkin, Romeo Ortega
It is also assumed that there is no a priori information about the disturbance or noise in the measurement channel (for example, frequency spectrum, covariance, etc.).
no code implementations • 11 Feb 2023 • Anton Pyrkin, Alexey Bobtsov, Romeo Ortega, Jose Guadalupe Romero, Denis Dochain
In this paper we provide the first solution to the challenging problem of designing a globally exponentially convergent estimator for the parameters of the standard model of a continuous stirred tank reactor.
no code implementations • 15 Sep 2022 • Alexey Bobtsov, Fernando Mancilla-David, Stanislav Aranovskiy, Romeo Ortega
While there are many results of identification of the parameters of the latter model, to the best of our knowledge, no one has provided a solution for the aforementioned more complex dynamic model since it concerns the parameter estimation of a nonlinear, underexcited system with unmeasurable state variables.
no code implementations • 14 Mar 2022 • Daniele Zonetti, Romeo Ortega, Rafael Cisneros, Alexey Bobtsov, Fernando Mancilla-David, Oriol Gomis-Bellmunt
We consider a Th\'evenin equivalent circuit capturing the dynamics of a power grid as seen from the point of common coupling with a power electronic converter, and provide a solution to the problem of online identification of the corresponding circuit parameters.
no code implementations • 14 Jan 2022 • Daniele Zonetti, Alexey Bobtsov, Romeo Ortega, Nikolay Nikolaev, Oriol Gomis-Bellmunt
In this paper we are interested in the problem of adaptive synchronization of a voltage source converter with a possibly weak grid with unknown angle and frequency, but knowledge of its parameters.
no code implementations • 19 Dec 2021 • Alexey Bobtsov, Romeo Ortega, Stanislav Aranovskiy, Rafael Cisneros
Wind turbines are often controlled to harvest the maximum power from the wind, which corresponds to the operation at the top of the bell-shaped power coefficient graph.
no code implementations • 10 Dec 2021 • Anton Pyrkin, Alexey Bobtsov, Romeo Ortega, Alberto Isidori
In this paper we are interested in the problem of adaptive state observation of linear time-varying (LTV) systems where the system and the input matrices depend on unknown time-varying parameters.
no code implementations • 19 Aug 2021 • Lei Wang, Romeo Ortega, Alexey Bobtsov, Jose Guadalupe Romero, Bowen Yi
The estimators are shown to be robust to additive measurement noise and--not necessarily slow--parameter variations.
no code implementations • 16 Jun 2021 • Marina Korotina, Jose Guadalupe Romero, Stanislav Aranovskiy, Alexey Bobtsov, Romeo Ortega
In this paper, we prove that it is possible to estimate online the parameters of a classical vector linear regression equation $ Y=\Omega \theta$, where $ Y \in \mathbb{R}^n,\;\Omega \in \mathbb{R}^{n \times q}$ are bounded, measurable signals and $\theta \in \mathbb{R}^q$ is a constant vector of unknown parameters, even when the regressor $\Omega$ is not persistently exciting.