A new paradigm for parameter estimation in system modeling

In this paper, we consider a basic problem in system identification, that of estimating the unknown parameters of a given model by using input/output data. Available methods (extended Kalman filtering, unscented Kalman filtering, particle filtering, maximum likelihood, prediction error method, etc.) have been extensively studied in the literature, especially in relation to consistency analysis. Yet, other important aspects, such as computational complexity, have been somewhat overlooked so that, when such methods are used in practical problems, remarkable drawbacks may arise. This is why parameter estimation is often performed using empirical procedures. This paper aims to revisit the issue of setting up an estimator that is able to provide reliable estimates at low computational cost. In contrast to other paradigms, the main idea in the new introduced two-stage estimation method is to retrieve the estimator through simulation experiments in a training phase. Once training is terminated, the user is provided with an explicitly given estimator that can be used over and over basically with no computational effort. The advantages and drawbacks of the two-stage approach as well as other traditional paradigms are identified with an illustrative example. A more concrete example of tire parameter estimation is also provided. Copyright (c) 2012 John Wiley & Sons, Ltd.