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Journal ArticleDOI

Model validation: a connection between robust control and identification

TL;DR: 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. >

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Citations
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Book
05 Oct 1997
TL;DR: In this article, the authors introduce linear algebraic Riccati Equations and linear systems with Ha spaces and balance model reduction, and Ha Loop Shaping, and Controller Reduction.
Abstract: 1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

3,471 citations

Journal ArticleDOI
TL;DR: A tutorial introduction to the complex structured singular value (μ) is presented, with an emphasis on the mathematical aspects of μ.

1,515 citations

Journal ArticleDOI
TL;DR: This work considers least-squares problems where the coefficient matrices A,b are unknown but bounded and minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A.
Abstract: We consider least-squares problems where the coefficient matrices A,b are unknown but bounded. We minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpreted as a Tikhonov regularization procedure, with the advantage that it provides an exact bound on the robustness of solution and a rigorous way to compute the regularization parameter. When the perturbation has a known (e.g., Toeplitz) structure, the same problem can be solved in polynomial-time using semidefinite programming (SDP). We also consider the case when A,b are rational functions of an unknown-but-bounded perturbation vector. We show how to minimize (via SDP) upper bounds on the optimal worst-case residual. We provide numerical examples, including one from robust identification and one from robust interpolation.

1,164 citations

Journal ArticleDOI
TL;DR: The theory complements model-based methods such as H/sup /spl infin//-robust control theory by providing a precise characterization of how the set of suitable controllers shrinks when new experimental data is found to be inconsistent with prior assumptions or earlier data.
Abstract: Without a plant model or other prejudicial assumptions, a theory is developed for identifying control laws which are consistent with performance objectives and past experimental data-possibly before the control laws are ever inserted in the feedback loop. The theory complements model-based methods such as H/sup /spl infin//-robust control theory by providing a precise characterization of how the set of suitable controllers shrinks when new experimental data is found to be inconsistent with prior assumptions or earlier data. When implemented in real time, the result is an adaptive switching controller. An example is included.

547 citations

Journal ArticleDOI
TL;DR: Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-input/multioutput) systems from frequency response data are presented.
Abstract: Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-input/multioutput) systems from frequency response data are presented. The algorithms are related to the recent time-domain subspace identification techniques. The first algorithm uses equidistantly, in frequency, spaced data and is strongly consistent under weak noise assumptions. The second algorithm uses arbitrary frequency spacing and is strongly consistent under more restrictive noise assumptions, promising results are obtained when the algorithms are applied to real frequency data originating from a large flexible structure.

536 citations

References
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DOI
John Doyle1
01 Nov 1982
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.
Abstract: The paper introduces a general approach for analysing linear systems with structured uncertainty based on a new generalised spectral theory for matrices. The results of the paper naturally extend techniques based on singular values and eliminate their most serious difficulties.

1,987 citations

Proceedings ArticleDOI
John Doyle1
01 Dec 1985
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.
Abstract: 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. The structured singular value, µ, plays a central role. The case where parameter variations are known to be real is considered.

532 citations

Journal ArticleDOI
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.
Abstract: The authors formulate and solve two related control-oriented system identification problems for stable linear shift-invariant distributed parameter plants In each of these problems the assumed a priori information is minimal, consisting only of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, and an upper bound on the noise level The first of these problems involves 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 with frequency corresponding to the frequency point of interest This problem leads naturally to the second problem, which involves identification of the plant transfer function in H/sub infinity / from a finite number of noisy point samples of the plant frequency response Concrete plans for identification algorithms are provided for each of these two problems >

512 citations