Estimation of nonlinear control parameters in induction machine using particle filtering

In this paper, particle filtering (PF) is addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. In the current work, the state variables (the rotor speed, the rotor flux, and the stator flux) as well as the model parameters are simultaneously estimated from noisy measurements of these variables, and the estimation technique is evaluated by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In this case, in addition to comparing the performances of the estimation, the effect of the number of estimated model parameters on the accuracy and convergence of this technique is also assessed. Simulation analysis demonstrates that the particle filter can well estimate the states/parameters under disturbs of the noise, and it provides efficient accuracies for the states estimation.

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