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Titlebook: Robustness in Identification and Control; A. Garulli (Assistant Professor),A. Tesi (Assistan Conference proceedings 1999 Springer-Verlag L

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Robustness and performance in adaptive filtering,pending on a drifting parameter. The mean-square and worst-case performances of the Normalized Least Mean Squares, Kalman, and central ..-filters are studied. The analysis shows that a compromise between performance and robustness should be pursued, for instance by applying the central ..-filter wit
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Nonlinear identification based on unreliable priors and data, with application to robot localizatioal data and priors. It is assumed that some of these tests may be unreliable, because of the approximate nature of the priors and of the presence of outliers in the data. The methodology presented, which can be applied to models nonlinear in their parameters, makes it possible to compute a set guara
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Robust model predictive control: A survey, the MPC literature, and the techniques proposed for robust constraint handling, stability, and performance. The key concept of “closedloop prediction” is discussed at length. The paper concludes with some comments on future research directions.
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Intrinsic performance limits of linear feedback control,inable .. norms of the sensitivity and complementary sensitivity functions. These results improve the previously known performance bounds and provide intrinsic limits on the performance of feedback systems irreducible via compensator design, leading to new insights toward the understanding of fundam
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Puzzles in systems and control,ation to robust control. The first puzzle deals with complications caused by fragility of poles and zeros in standard parameterizations of systems due to unavoidable parameter inaccuracies. The second and the third puzzle are concerned with difficulties in generalizing robust .. control theory to re
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Robust regional pole placement: An affine approximation,sidered. Owing to a special upper triangular form of the desired closed-loop polynomial matrix, this formulation leads to an affine design problem. This design is closer to the ultimate pole placement than the assignment of the characteristic polynomial direct. As a result, one obtains a set of affi
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Towards a harmonic blending of deterministic and stochastic frameworks in information processing,on as an example, we demonstrate the application and utility of the methodology. Lower and upper bounds on identification errors are obtained for systems subject to both deterministic unmodelled dynamics and random external disturbances.
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Nonlinear identification based on unreliable priors and data, with application to robot localizationteed to contain all values of the parameter vector that would have been obtained if all tests had been reliable, provided that an upper bound on the number of faulty tests is available. This remains true even if a majority of the tests turns out to be faulty. The methodology is applied to the localization of a robot from distance measurements.
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