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
More filters
Journal ArticleDOI
Derivation and selection of norm-bounded uncertainty descriptions based on multiple models
TL;DR: An approach for non-conservative filter design by optimizing a closed-loop criterion is proposed and is highlighted by a design example, where additive, input-multiplicative and output- multiplicative uncertainty models are compared.
Journal ArticleDOI
A local rational model approach for H ∞ norm estimation:With application to an active vibration isolation system
Egon Geerardyn,Tae Tom Oomen +1 more
TL;DR: In this article, the authors developed an approach for accurate uncertainty modeling based on H∞-norm estimation, or peak amplitude, which takes into account inter-grid frequency behavior while only requiring a reduced experiment time, modeling effort, and limited user intervention.
Proceedings ArticleDOI
An LPV approach to synthesizing robust active vision systems
TL;DR: In this paper, a linear parameter varying (LPV) form is used to recast the problem into a linear parameters varying form and using recently developed robust identification and control tools for this class of problems.
Proceedings ArticleDOI
Application of LPV/LFT Modeling and Data-based Validation to a re-entry vehicle
Andres Marcos,Murray Kerr,G. De Zaiacomo,Luis F. Peñín,Zoltán Szabó,Gábor Rödönyi,József Bokor +6 more
TL;DR: In this article, an application of LFT/LPV modeling and data-based validation techniques to a re-entry vehicle is shown, where the selected vehicle is the longitudinal nonlinear motion of NASA HL-20 during an approach trajectory from Mach 4.5 down to 1.5.
Proceedings ArticleDOI
Model (in)validation and fault detection for systems with polynomial state-space models
TL;DR: This paper presents asymptotically tight invalidation certificates and shows how a model-based run-time fault detection algorithm can be developed based on a notion of T-detectability, which enables the proposed model invalidation approach to be applied in receding horizon fashion to detect faults.
References
More filters
Analysis of feedback systems with structured uncertainties
TL;DR: In this article, a general approach for analysing linear systems with structured uncertainty based on a new generalised spectral theory for matrices is introduced, which naturally extend techniques based on singular values and eliminate their most serious difficulties.
Proceedings ArticleDOI
Structured uncertainty in control system design
TL;DR: This paper reviews control system analysis and synthesis techniques for robust performance with structured uncertainty in the form of multiple unstructured perturbations and parameter variations in the case where parameter variations are known to be real.
Journal ArticleDOI
Control oriented system identification: a worst-case/deterministic approach in H/sub infinity /
TL;DR: The authors formulate and solve two related control-oriented system identification problems for stable linear shift-invariant distributed parameter plants, each involving identification of a point sample of the plant frequency response from a noisy, finite, output time series obtained in response to an applied sinusoidal input.