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  • 1
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    Validation of an Experimentally Derived Uncertainty Model (1996)
    In:  CASI
    Details
    Publication Date: 2018-06-02
    Description: The results show that uncertainty models can be obtained directly from system identification data by using a minimum norm model validation approach. The error between the test data and an analytical nominal model is modeled as a combination of unstructured additive and structured input multiplicative uncertainty. Robust controllers which use the experimentally derived uncertainty model show significant stability and performance improvements over controllers designed with assumed ad hoc uncertainty levels. Use of the identified uncertainty model also allowed a strong correlation between design predictions and experimental results.
    Keywords: Numerical Analysis
    Format: application/pdf
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  • 2
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    On the Directional Dependence and Null Space Freedom in Uncertainty Bound Identification (1997)
    In:  CASI
    Details
    Publication Date: 2019-07-10
    Description: In previous work, the determination of uncertainty models via minimum norm model validation is based on a single set of input and output measurement data. Since uncertainty bounds at each frequency is directionally dependent for multivariable systems, this will lead to optimistic uncertainty levels. In addition, the design freedom in the uncertainty model has not been utilized to further reduce uncertainty levels. The above issues are addressed by formulating a min- max problem. An analytical solution to the min-max problem is given to within a generalized eigenvalue problem, thus avoiding a direct numerical approach. This result will lead to less conservative and more realistic uncertainty models for use in robust control.
    Keywords: Numerical Analysis
    Type: AIAA Paper 97-3460
    Format: application/pdf
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