Robust state estimation in uncertain systems: Combined detection estimation with incremental MSE criterion

Sebald,; Haddad,

doi:10.1109/cdc.1976.267682

Cited by 3 publications

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“…Also, besides stability, no further dynamic aspects are considered. The authors of also considered systems with uncertain parameters and proposed a computational procedure yielding an optimal estimator for state estimation. With the advent of more powerful computers, the area of robust control received much attention (e.g., ), where control synthesis methods are proposed for systems with time‐varying parameters as well.…”

Section: Introductionmentioning

confidence: 99%

Using LMIs to optimize robustness of observer‐based state‐feedback for a synchrotron

Gering

Grieser

Wahrburg

2013

Intl J Robust & Nonlinear

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SUMMARYIn this article, a synthesis method for linear systems subject to uncertain time-varying parameters is proposed. Starting with the given values for the nominal parameters, the first objective is to find a constant state feedback such that the controlled system is stabilized and given dynamic specifications, such as a minimal decay rate, are met. In a second step, the constant feedback is then locally optimized such that the uncertain system parameters are allowed to vary around their nominal point as much as possible, whereas stabilization and dynamic specifications still hold. In addition, the procedure is extended toward observer-based state feedback. Finally, this approach is applied to a synchrotron example, where the particle beam in longitudinal direction is to be stabilized and the coherent synchrotron frequency as well as the damping rate are uncertain.

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Section: Introductionmentioning

confidence: 99%

Using LMIs to optimize robustness of observer‐based state‐feedback for a synchrotron

Gering

Grieser

Wahrburg

2013

Intl J Robust & Nonlinear

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State estimation for singularly perturbed systems with uncertain perturbation parameter

Sebald

Haddad

1978

IEEE Trans. Automat. Contr.

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Forecasting and Control. K. J. Astrom and T. Bohlis "Numerical identification of linear dynamic systems from normal operating records," in Prm. IFAC Conf. Se~-Adapriw Control System (Teddington, England). 1965. C. M. Woodside, "Estimation of the order of linear system" Automatxa, uol. 7, pp. 727-733, 1971. D. Graupe. Identification of System. New York: Van Nostrand-Reinbold, 1972. A. Akaike, "A new look at the statistical model identification," IEEE T m . A canonical model for identification of multivariable linear syseconometrics; Similarities and differences,* Ann. Econ. Soc. M e a . . pp. 2147, 1974. R. K. Mebra, Topics in stochastic control theory identification in control and multivariable stochastic linear system" IEEE Trans. Auromat. Contr., vol. AC-20, E. Tse and H. Weinerf "Structure det-ation and parameter identificabon for pp. 603-613, 1975. R. Liu and L. C. Suen, "Minimal dimension realization and identifiability of input/output sequences," IEEE Trans. A u t o m . Contr., Apr. 1977. L. C. S u e s "Minimd-dimension-realization method of system identification," Pb.D. dissertation, Univ. Notre Dame, Notre Dame, IN, 1977. C. R. Rao, Linear Sraristicaf Inference and Irs Application. Kew York: Wiley. 1965. D. M. Wiberg, Stare Spore and Linear System. New York: McGraw-Hill, 1971.estimator estimates in multi-input muki-output discrete-time linear systems," IEEE

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On state estimation for linear discrete-time systems with unknown noise covariances

Tugnait

Haddad

1979

IEEE Trans. Automat. Contr.

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Robust state estimation in uncertain systems: Combined detection estimation with incremental MSE criterion

Cited by 3 publications

References 0 publications

Using LMIs to optimize robustness of observer‐based state‐feedback for a synchrotron

Using LMIs to optimize robustness of observer‐based state‐feedback for a synchrotron

State estimation for singularly perturbed systems with uncertain perturbation parameter

On state estimation for linear discrete-time systems with unknown noise covariances

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