Alexey Bobtsov

SY
h-index33
3papers
101citations
Novelty35%
AI Score21

3 Papers

1.2SYNov 7, 2017
State Observers for Sensorless Control of Magnetic Levitation Systems

Alexey Bobtsov, Anton Pyrkin, Romeo Ortega et al.

In this paper we address the problem of state observation for sensorless control of nonlinear magnetic levitation systems, that is, the regulation of the position of a levitated object measuring only the voltage and current of the electrical supply. Instrumental for the development of the theory is the use of parameter estimation-based observers, which combined with the dynamic regressor extension and mixing parameter estimation technique, allow the reconstruction of the magnetic flux. With the knowledge of the latter it is shown that the mechanical coordinates can be estimated with suitably tailored nonlinear observers. Replacing the observed states, in a certainty equivalent manner, with a full information globally stabilising law completes the sensorless controller design. We consider one and two-degrees-of-freedom systems that, interestingly, demand totally different mathematical approaches for their solutions. Simulation results are used to illustrate the performance of the proposed schemes.

1.2SYApr 29, 2016
Identification of Photovoltaic Arrays' Maximum Power Extraction Point via Dynamic Regressor Extension and Mixing

Anton Pyrkin, Fernando Mancilla-David, Romeo Ortega et al.

This paper deals with the problem of identification of photovoltaic arrays' maximum power extraction point---information that is encrypted in the current-voltage characteristic equation. We propose a new parameterisation of the classical five parameter model of this function that, combined with the recently introduced identification technique of dynamic regressor extension and mixing, ensures a fast and accurate estimation of all unknown parameters. A concavity property of the current-voltage characteristic equation is then exploited to directly identify the desired voltage operating point. Realistic numerical examples via computer simulations are presented to assess the performance of the proposed approach.

1.2SYDec 10, 2021
An Adaptive Observer for Uncertain Linear Time-Varying Systems with Unknown Additive Perturbations

Anton Pyrkin, Alexey Bobtsov, Romeo Ortega et al.

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. It is assumed that these parameters satisfy some known LTV dynamics, but with unknown initial conditions. Moreover, the state equation is perturbed by an additive signal generated from an exosystem with uncertain constant parameters. Our main contribution is to propose a globally convergent state observer that requires only a weak excitation assumption on the system.