Model validation: a connection between robust control and identification
Roy S. Smith,John Doyle +1 more
TLDR
The model validation problem addressed is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the observed input-output data?Abstract:
The gap between the models used in control synthesis and those obtained from identification experiments is considered by investigating the connection between uncertain models and data. The model validation problem addressed is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the observed input-output data? This problem is studied for the standard H/sub infinity // mu framework models. A necessary condition for such a model to describe an experimental datum is obtained. For a large class of models in the robust control framework, this condition is computable as the solution of a quadratic optimization problem. >read more
Citations
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Book ChapterDOI
A note on H ∞ system identification with probabilistic apriori information
Clas A. Jacobson,Gilead Tadmor +1 more
TL;DR: It is shown that the Probabilistic H∞ problem is equivalent to a worst case problem that is constructed from the probabilistic one and allows near optimal algorithms to be constructed for the probablistic H ∞, identification problem.
Proceedings ArticleDOI
Coprime factor based closed-loop model validation
TL;DR: In this paper, the authors provide a formal definition of the model validation problem and provide more details on an uncertainty model that consists of a nominal model and an additional model uncertainty or allowable model perturbation.
Journal ArticleDOI
Model Validation: A Novel Approach to Fault Detection
S. Thapliyal,J.C. Kantor +1 more
TL;DR: In this paper, the authors address a fault detection application of model validation using the l1 formulation, in which deterministic bounds on model uncertainty are assumed on the minimum disturbance and perturbation consistent with observed data.
Journal ArticleDOI
Self-Tuning Control by Model Unfalsification
Sandor M. Veres,Hao Xia +1 more
TL;DR: In this article, a non-stochastic framework based on model unfalsification is proposed for self-tuning control, which combines model-falsification with the application of a controller with the best robust performance.
References
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Proceedings ArticleDOI
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