A nonlinear least-squares approach for identification of the induction motor parameters

A nonlinear least-squares method is presented for the identification of the induction motor parameters. A major difficulty with the induction motor is that the rotor state variables are not available measurements so that the system identification model cannot be made linear in the parameters without overparametrizing the model. Previous work in the literature has avoided this issue by making simplifying assumptions such as a "slowly varying speed." Here, no such simplifying assumptions are made. The problem is formulated as a nonlinear least-squares identification problem and uses elimination theory (resultants) to compute the parameter vector that minimizes the residual error. The only requirement is that the system must be sufliciently excited. The method is suitable for online operation to continuously update the parameter values. Experimental results are presented.