Search Results for author:
Found 13 papers, 0 papers with code
Parameter identification algorithm for a LTV system with partially unknown state matrix
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.
On-line Parameter Estimation of the Polarization Curve of a Fuel Cell with Guaranteed Convergence Properties: Theoretical and Experimental Results
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.
Application of a nonlinear operator to identify an unknown parameter for a scalar regression equation with disturbance in the measurement channel
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.
Adaptive observer of state variables of a nonlinear time varying system with unknown constant parameters
The paper proposes an adaptive observer of the state vector of a nonlinear time varying system based on measurements of the output variable.
Synthesis of an adaptive observer of state variables for a linear stationary object in the presence of measurement noise
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.).
A Globally Convergent Estimator of the Parameters of the Classical Model of a Continuous Stirred Tank Reactor
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.
On-line Identification of Photovoltaic Arrays' Dynamic Model Parameters
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.
An Observer-Based Composite Identifier for Online Estimation of the Thevenin Equivalent Parameters of a Power System
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.
An Almost Globally Stable Adaptive Phase-Locked Loop for Synchronization of a Grid-Connected Voltage Source Converter
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.
On-line Estimation of the Parameters of the Windmill Power Coefficient
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.
An Adaptive Observer for Uncertain Linear Time-Varying Systems with Unknown Additive Perturbations
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.
Identifiability Implies Robust, Globally Exponentially Convergent On-line Parameter Estimation: Application to Model Reference Adaptive Control
The estimators are shown to be robust to additive measurement noise and--not necessarily slow--parameter variations.
Persistent Excitation is Unnecessary for On-line Exponential Parameter Estimation: A New Algorithm that Overcomes this Obstacle
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.
