Output Adaptive Observers Design for Linear Non-Stationary Systems with Polynomial Parameters
https://doi.org/10.17587/mau.22.404-410
Abstract
The article deals with the problem of state observer design for a linear time-varying plant. To solve this problem, a number of realistic assumptions are considered, assuming that the model parameters are polynomial functions of time with unknown coefficients. The problem of observer design is solved in the class of identification approaches, which provide transformation of the original mathematical model of the plant to a static linear regression equation, in which, instead of unknown constant parameters, there are state variables of generators that model non-stationary parameters. To recover the unknown functions of the regression model, we use the recently well-established method of dynamic regressor extension and mixing (DREM), which allows to obtain monotone estimates, as well as to accelerate the convergence of estimates to the true values. Despite the fact that the article deals with the problem of state observer design, it is worth noting the possibility of using the proposed approach to solve an independent and actual estimation problem of unknown time-varying parameters.
About the Authors
K. B. Dang
ITMO University
Russian Federation
Russian Federation
St. Petersburg, 197101
A. A. Pyrkin
ITMO University
Russian Federation
Russian Federation
Pyrkin Anton A., Dr. Sc., Professor
St. Petersburg, 197101
A. A. Bobtsov
ITMO University
Russian Federation
Russian Federation
St. Petersburg, 197101
A. A. Vedyakov
ITMO University
Russian Federation
Russian Federation
St. Petersburg, 197101
S. I. Nizovtsev
ITMO University
Russian Federation
Russian Federation
St. Petersburg, 197101
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Review
For citations:
Dang K.B., Pyrkin A.A., Bobtsov A.A., Vedyakov A.A., Nizovtsev S.I. Output Adaptive Observers Design for Linear Non-Stationary Systems with Polynomial Parameters. Mekhatronika, Avtomatizatsiya, Upravlenie. 2021;22(8):404-410. (In Russ.) https://doi.org/10.17587/mau.22.404-410
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