Abstract:
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is...Show MoreMetadata
Abstract:
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.
Published in: 2016 IEEE Conference on Control Applications (CCA)
Date of Conference: 19-22 September 2016
Date Added to IEEE Xplore: 13 October 2016
ISBN Information: