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.
Original languageEnglish
Pages2711-2714
Number of pages4
DOIs
StatePublished - 1 Jul 2024
Event10th International Conference on Control, Decision and Information Technologies - Valletta, Malta
Duration: 1 Jul 20244 Jul 2024

Conference

Conference10th International Conference on Control, Decision and Information Technologies
Abbreviated titleCoDIT 2024
Country/TerritoryMalta
CityValletta
Period1/07/244/07/24

    Research areas

  • Equations of state, Friction, Regression analysis, Adaptive observer, Adaptive state observer, Equation parameter, Induction motor modeling, Original model, Parameter variable, State parameters, State-variables, Unknown state, Viscous friction, State estimation

ID: 127409031