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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Proceedings ArticleDOI
L 1 - L 2 robust estimation in prediction error system identification
TL;DR: A mixed L1 -L2 estimator based on a parameterized objective function leading to an alternative solution fighting against the outliers, based on the well-known Huber's M-estimate is presented.
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A probabilistic approach to model set validation
TL;DR: A probabilistic measure named model set unfalsified probability (MSUP) for model set validation, where the model set is described by an LFT (linear fractional transformation) form, is introduced.
Book ChapterDOI
Sampling random transfer functions
TL;DR: This paper exploits both time and frequency domain characterizations to develop efficient algorithms for generation of random samples of causal, linear time-invariant uncertain transfer functions for systems subject to structured dynamic uncertainty.
Proceedings ArticleDOI
A pessimistic approach to frequency domain model (in)validation
Necmiye Ozay,Mario Sznaier +1 more
TL;DR: A pessimistic approach is taken, where the smallest ball is sought such that the resulting model is invalidated by the experimental data for all uncertainties outside this ball, so that a robust controller designed using this information is guaranteed to stabilize the unknown plant.
Journal ArticleDOI
Modelling of Uncertain Systems via Linear Programming
T. K. Gustafsson,Pertti Mäkilä +1 more
TL;DR: In this article, the authors present linear programming methods for identification and model validation for uncertain systems, including smoothness priors, Laguerre-model identification and unmodelled dynamics.
References
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