Kalman filtering with unknown inputs via optimal state estimation of singular systems

DOI10.1080/00207729508929152zbMath0834.93053OpenAlexW2069705468MaRDI QIDQ4852975

Mohamed Darouach, A. Bassong Onana, S. Nowakowski, Michel Zasadzinski

Publication date: 5 March 1996

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207729508929152

zbMATH Keywords

Kalman filtersingular systemsdiscrete-timeunknown inputs

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Multivariable systems, multidimensional control systems (93C35)

Related Items (15)

Robust fault diagnosis with a two-stage Kalman estimator ⋮ Framework for state and unknown input estimation of linear time-varying systems ⋮ Multiple window moving horizon estimation ⋮ State estimation for stochastic discrete-time systems with multiplicative noises and unknown inputs over fading channels ⋮ Robust state estimation and unknown inputs reconstruction for a class of nonlinear systems: multiobjective approach ⋮ A stochastic unknown input realization and filtering technique ⋮ Distributed fusion robust filter for multisensor networked singular control system with uncertain variances and missing measurement ⋮ Observers design for a class of nonlinear singular systems ⋮ State estimation for descriptor systems via the unknown input filtering method ⋮ State estimation for sampled-data descriptor nonlinear system: a strong tracking unscented Kalman filter approach ⋮ Adaptive observers for linear stochastic time‐variant systems with disturbances ⋮ Optimal unbiased reduced order filtering for discrete-time descriptor systems via LMI ⋮ Weighted measurement fusion Kalman estimator for multisensor descriptor system ⋮ Unknown input observers for uncertain systems: a unifying approach. ⋮ H_∞filter design for a class of nonlinear discrete-time singular systems

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• Kalman filtering and Riccati equations for descriptor systems
• Observing the states of systems with unmeasurable disturbances
• Adaptive sequential estimation with unknown noise statistics
• State estimation of stochastic singular linear systems
• Observers for linear systems with unknown inputs

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