Abstract
For a large class of systems, the nonlinear identification problem can be turned into a linear regression of an overparameterized model, leading to several estimators for the actual ones. In this note, a simple and useful approach is proposed to obtain a global solution and, in addition, different estimators can be combined into a new one that is less sensitive to modeling and measurement errors. This issue is presented and applied to the induction motor. Numerical results show that sensivity to measurement and modelling errors is similar to an output error method; moreover. this sensitivity can be drastically reduced by combining several estimators.
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