Y.K. Lim, J.-W. Kim, and S.W. Kim (Korea)
parameter identification, induction motor, prediction error method, genetic algorithm
In this paper, a continuous-time prediction error method is applied to the parameter identification of an induction motor. Parameter update laws are designed with gradient vectors and Hessian matrices. Digital simulations are conducted on startup with no-load, and the simulated sig nals are used to identify true parameter values. A genetic algorithm is compared with the continuous-time prediction error method, because both algorithms are capable to identify whole motor parameter values simultaneously. Identification result shows that the prediction error method is considerably faster than the genetic algorithm with similar estimation errors.
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