IDENTIFICATION OF ARX MODELS WITH TIME-VARYING BOUNDED PARAMETERS: A SEMIDEFINITE PROGRAMMING APPROACH

摘要

In this paper, we develop a new identification procedure for ARX models with uncertain, unknown-but-bounded time-varying coefficients. The method seeks the smallest ellipsoid containing the coefficients, such that the resulting model in unfal-sified by the observed data. The problem is formulated as a semidefinite programming program. We interpret our deterministic method in a probabilistic setting, and show how this interpretation can be used to effectively discard outliers. We generalize the method to multivariate models with matrix unstructured uncertainty. The resulting model is directly amenable to worst-case simulation and robust control techniques.