Modeling of normalized coprime factors with H∞-norm bounded uncertainty
Modeling of uncertain systems with normalized coprime factor description is investigated where the experimental data is given by a finite set of frequency response measurement samples of the open loop plant that is possibly infinite-dimensional. The objective is not only to identify the nominal model but also to quantify the modeling error with H∞ norm bounds in form of normalized coprime factor uncertainty compatible with robust control. This work is an extension of robust identification in H∞ proposed in where the uncertainty is quantified in additive form. An algorithm is developed to model the nominal normalized coprime factor of the given plant using techniques of discrete Fourier analysis (DFA) and balanced stochastic truncation (BST) and are shown to be robust. Upper bounds are derived for the associated modeling error. It is interesting to see that the proposed modeling approach is suitable to identification of flexible structures which are inherently infinite-dimensional.
Publication Source (Journal or Book title)
Proceedings of the IEEE Conference on Decision and Control
Gu, G. (1994). Modeling of normalized coprime factors with H∞-norm bounded uncertainty. Proceedings of the IEEE Conference on Decision and Control, 4, 3955-3960. Retrieved from https://repository.lsu.edu/eecs_pubs/374