Analysis of Continuous-time Robust Control Models using Finite, Sampled, Experimental Data
Open access
Date
1995Type
- Report
ETH Bibliography
no
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Abstract
The application of robust control theory requires applicable models containing unknown, bounded, perturbations and unknown, bounded input signals. Model validation is a quantitative means of assessing the applicability of a given model with respect to experimental data.
This paper develops a theoretical framework, and a computational solution, for the model validation problem in the case where the model, including unknown perturbations and signals, is given in the continuous time, yet the experimental datum is a finite, sampled, signal. The continuous nature of the unknown components is treated directly with a sampled data lifting theory. This gives results which are valid for any sample period and any datum length. Explicit calculation of whether sufficient data for invalidation has been obtained arises naturally in this framework. A common class of robust control models is treated in both open- and closed-loop and yields a convex matrix optimization problem. A simulation, and an experimental, example illustrate the approach. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000672706Publication status
publishedJournal / series
Center for Control Engineering & Computation. Technical ReportVolume
Publisher
University of California Santa BarbaraOrganisational unit
08814 - Smith, Roy (Tit.-Prof.)
Related publications and datasets
Is supplement to: https://doi.org/10.3929/ethz-b-000677206
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ETH Bibliography
no
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