TY - CONF

T1 - DREM-based Adaptive Observer for Induction Motor Model With Friction

AU - Bespalov, V.

AU - Vedyakov, A.

AU - Vediakova, A.

N1 - Код конференции: 203436 Export Date: 18 November 2024 Сведения о финансировании: Ministry of Education and Science of the Russian Federation, Minobrnauka, 2019-0898 Сведения о финансировании: Ministry of Education and Science of the Russian Federation, Minobrnauka Текст о финансировании 1: This work was supported by the Ministry of Science and Higher Education of Russian Federation, passport of goszadanie no. 2019-0898.

PY - 2024/7/1

Y1 - 2024/7/1

N2 - This paper presents an adaptive state observer for a nonlinear induction motor model that accounts for viscous friction. The problem is solved using a modified version of the Dynamic Regressor Extension and Mixing (DREMBAO) method. The main idea is to reduce the original model to a regression-like model, where the vector of unknowns contains unknown parameters and state variables. After this step, it becomes possible to obtain a set of scalar linear equations with respect to the unknown state variables and parameters. Using these equations, parameters are estimated with gradient descent estimator, and state estimation is obtained using the gradient observer. Simulation results of an adaptive observer are presented, which demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

AB - This paper presents an adaptive state observer for a nonlinear induction motor model that accounts for viscous friction. The problem is solved using a modified version of the Dynamic Regressor Extension and Mixing (DREMBAO) method. The main idea is to reduce the original model to a regression-like model, where the vector of unknowns contains unknown parameters and state variables. After this step, it becomes possible to obtain a set of scalar linear equations with respect to the unknown state variables and parameters. Using these equations, parameters are estimated with gradient descent estimator, and state estimation is obtained using the gradient observer. Simulation results of an adaptive observer are presented, which demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

KW - Equations of state

KW - Friction

KW - Regression analysis

KW - Adaptive observer

KW - Adaptive state observer

KW - Equation parameter

KW - Induction motor modeling

KW - Original model

KW - Parameter variable

KW - State parameters

KW - State-variables

KW - Unknown state

KW - Viscous friction

KW - State estimation

U2 - 10.1109/codit62066.2024.10708471

DO - 10.1109/codit62066.2024.10708471

M3 - материалы

SP - 2711

EP - 2714

Y2 - 1 July 2024 through 4 July 2024

ER -