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P. Dooren (1984)
Reduced order observers: A new algorithm and proofSystems & Control Letters, 4
J. Watson, K. Grigoriadis (1997)
Optimal unbiased filtering via linear matrix inequalitiesProceedings of the 1997 American Control Conference (Cat. No.97CH36041), 5
X. Ding, Paul Frank, L. Guo (1990)
Robust observer design via factorization approach29th IEEE Conference on Decision and Control
M. Vidyasagar
Control Systems Synthesis: A Factorization Approach
Chen Wang, G. Weiss (2006)
Self-Scheduled LPV Control of a Wind Driven Doubly-Fed Induction GeneratorProceedings of the 45th IEEE Conference on Decision and Control
A. Rahmani (2010)
Synthèse d'observateurs
S. Ding, Limin Guo, P. Frank (1994)
Parameterization of linear observers and its application to observer designIEEE Trans. Autom. Control., 39
P. Hippe (1991)
Design of observer based compensators: The polynomial approachKybernetika, 27
Chia-Chi Tsui (1985)
A new algorithm for the design of multifunctional observersIEEE Transactions on Automatic Control, 30
L. Dai (1989)
Singular Control Systems, 118
D. Russell, T. Bullock (1975)
A frequency domain approach to minimal-order observer design for several linear functions of the state1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes
M. Darouach, M. Zasadzinski, H. Ali (2001)
Robust reduced order unbiased filtering via LMI2001 European Control Conference (ECC)
S. Boyd, L. El Ghaoui, E. Feron, V. BalaKrishnan
Linear Matrix Inequality in Systems and Control Theory
Harouna Ali (2002)
Observateurs robustes d'ordre réduit pour les systèmes linéaires et bilinéaires incertains
Gaomin Zhang, Yuanqing Xia, P. Shi (2008)
Technical communique: New bounded real lemma for discrete-time singular systemsAutomatica, 44
A. MacFarlane (1971)
Return-difference matrix properties for optimal stationary Kalman-Bucy filter, 118
M. Vidyasagar (1985)
Control System Synthesis
P. Hippe (1989)
Design of reduced-order optimal estimators directly in the frequency domainInternational Journal of Control, 50
J. Moore, G. Ledwich (1975)
Minimal order observers for estimating linear functions of a state vectorIEEE Transactions on Automatic Control, 20
D. Luenberger (1966)
Observers for multivariable systemsIEEE Transactions on Automatic Control, 11
P. Hippe, J. Deutscher (2009)
Design of Observer-based Compensators: From the Time to the Frequency Domain
P. Hippe, C. Wurmthaler (1990)
Optimal reduced-order estimators in the frequency domain: the discrete-time caseInternational Journal of Control, 52
J. O'Reilly (1983)
Observers for Linear Systems
M. Darouach, M. Zasadzinski (2009)
Optimal unbiased reduced order filtering for discrete-time descriptor systems via LMISyst. Control. Lett., 58
B. Anderson, V. Kučera (1985)
Matrix fraction construction of linear compensatorsIEEE Transactions on Automatic Control, 30
Shou-Yuan Zhang (1987)
Functional observer and state feedbackInternational Journal of Control, 46
M. Darouach (2000)
Existence and design of functional observers for linear systemsIEEE Trans. Autom. Control., 45
Stephen Boyd, L. Ghaoui, E. Feron, V. Balakrishnan (1994)
Linear Matrix Inequalities in System and Control Theory [Book Reviews]IEEE Transactions on Automatic Control, 42
Z. Xiang, Ronghao Wang (2009)
Non-fragile observer design for nonlinear switched systems with time delayInt. J. Intell. Comput. Cybern., 2
G. Zhang, Y. Xia, P. Shi
New bounded real lemma for discrete‐time singular systems
Purpose – The purpose of this paper is to propose solutions for both discrete‐time and frequency‐domain designs of unbiased H ∞ functional filters for discrete‐time linear systems affected by bounded norm energy disturbances. Design/methodology/approach – The discrete‐time procedure design is based on the unbiasedness of the functional filter using a Sylvester equation; then the problem is expressed in a singular system one and is solved in terms of linear matrix inequalities (LMIs). The frequency procedure design is derived from discrete‐time domain results by defining some useful matrix fraction descriptions and mainly, establishing the useful and equivalent form of the connecting relationship that parameterizes the dynamics behavior between discrete‐time and z‐domain. Findings – The performance of the proposed approach is illustrated with the aid of a practical example. The proposed methods are easily implementable and concern a more general class of systems, as the transformation of the system in a singular one permits to treat the problem of perturbance advanced. Originality/value – First, the order of this filter is equal to the dimension of the vector to be estimated, which is benefit in case of control purpose (reduction of time calculation comparing to the full order one). Second, all recent works on the functional filtering consider systems which permit to avoid to have advanced perturbation term in the error dynamics; the authors propose here an approach which resolves the H ∞ filtering problem even when the term is present. In addition, it permit to consider more general class of discrete‐time systems. Furthermore, the LMI approaching the discrete‐time case permits to handle with more general problem ( H ∞ , L 2 − H ∞ ) than the classical Riccati one.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Aug 23, 2011
Keywords: Discrete‐time domain; Frequency domain; Linear system; Unbiased H ∞ filtering; Linear matrix inequalities; Matrix fractions; Design; Control systems
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