A. H. Sayed. A framework for state-space estimation with uncertain models. IEEE Trans. Auto. Contr., 46(7):998--1013, July 2001. Home/Search Document Details and Download
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Design of Robust Filters with Improved Robustness Margins via.. - Terra, Sayed (2001) Self-citation (Sayed) (Correct)
....His work was supported in part by NSF Award ECS 9820765. 6, 7] Both classes of filters involve certain parameters that need to be adjusted and that define the robustness levels of the filters, e.g. the parameter fl in H1 filtering and the parameter ffl in guaranteed cost designs (see also [8]) For H1 filters it is necessary to decrease the value of fl for increased robustness, while for guaranteed cost filters it is necessary to increase the value of ffl for increased robustness. However, there are limits on how far these parameters can be adjusted without violating certain existence ....
....it is necessary to increase the value of ffl for increased robustness. However, there are limits on how far these parameters can be adjusted without violating certain existence conditions that are associated with such filters. The third class of robust filters we study is the one developed in [8]; it is based on minimizing the worst case residual energies at each iteration subject to bounds on data uncertainties. The filters of [8] differ from H1 and guaranteed cost filters in that they perform data regularization as opposed to data de regularization. In this way, they are particularly ....
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A. H. Sayed. A framework for state-space estimation with uncertain models. IEEE Trans. Automat. Contr., vol. 46, no. 7, pp. 998--1013, July 2001.
A Regularized Robust Design Criterion for Uncertain Data - Sayed, Nascimento, Cipparrone (2002) Self-citation (Sayed) (Correct)
....appear in [5] 8] we refer the reader to these articles for motivation, examples, simulations, and comparisons with other related techniques. As a brief motivation, one application in the context of state space estimation is succinctly described in Sec. 2. 3, with full details provided in [6]. In most of the paper, however, we opt to focus on studying the properties and technical aspects of the robust least squares problem that is formulated further ahead in (2.1) As mentioned above, the formulation in this article is useful for at least two reasons. First, it leads to a robust ....
....(2.1) As mentioned above, the formulation in this article is useful for at least two reasons. First, it leads to a robust solution that involves regularization rather than de regularization. In this way, existence conditions do not arise, which could be a burden for on line solutions (see, e.g. [6, 7]) Second, the framework incorporates both regularization and weighting into the cost function. Such extensions are needed in order to handle, for example, quadratic control and estimation problems where regularization and weighting are prevalent (see, e.g. 5, 6, 7, 10] 2. PROBLEM ....
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A. H. Sayed, "A framework for state-space estimation with uncertain models," IEEE Transactions on Automatic Control, vol. 46, no. 7, pp. 998--1013, July 2001.
Robust Exponential Filtering for Uncertain Systems with.. - Subramanian, Sayed (2002) Self-citation (Sayed) (Correct)
....appreciably. This filter sensitivity to modeling errors has led to several works in the literature on the development of robust state space filters; robust in the sense that they attempt to limit, in certain ways, the effect of model uncertainties on the overall filter performance; see for example [2] [8] In this paper, we move beyond earlier robust formulations and design a robust filter for linear systems with mixed stochastic and deterministic parametric uncertainties in the state space model. Robustness is enforced by ensuring exponential stability of the error system in the mean square ....
A. H. Sayed. A framework for state space estimation with uncertain models. IEEE Trans. Automat. Contr., vol. 46, no. 7, pp. 998--1013, July 2001.
Robust H 2 and - Filters For Uncertain (Correct)
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A. H. Sayed. A framework for state-space estimation with uncertain models. IEEE Trans. Auto. Contr., 46(7):998--1013, July 2001.
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