Parametric Identification Using Weighted Null-Space Fitting
For control engineers and system identification practitioners, this provides a computationally simpler alternative to non-convex optimization with equivalent asymptotic properties.
The paper proves that Weighted Null-Space Fitting (WNSF) for system identification yields asymptotically efficient estimates equivalent to prediction error methods with quadratic cost, and simulations show it is competitive with state-of-the-art methods.
In identification of dynamical systems, the prediction error method using a quadratic cost function provides asymptotically efficient estimates under Gaussian noise and additional mild assumptions, but in general it requires solving a non-convex optimization problem. An alternative class of methods uses a non-parametric model as intermediate step to obtain the model of interest. Weighted null-space fitting (WNSF) belongs to this class. It is a weighted least-squares method consisting of three steps. In the first step, a high-order ARX model is estimated. In a second least-squares step, this high-order estimate is reduced to a parametric estimate. In the third step, weighted least squares is used to reduce the variance of the estimates. The method is flexible in parametrization and suitable for both open- and closed-loop data. In this paper, we show that WNSF provides estimates with the same asymptotic properties as PEM with a quadratic cost function when the model orders are chosen according to the true system. Also, simulation studies indicate that WNSF may be competitive with state-of-the-art methods.