H∞ filtering with randomly occurring sensor saturations and missing measurements

In this paper, the H∞ filtering problem is investigated for a class of nonlinear systems with randomly occurring incomplete information. The considered incomplete information includes both the sensor saturations and the missing measurements. A new phenomenon of sensor saturation, namely, randomly oc...

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Veröffentlicht in:Automatica (Oxford) Jg. 48; H. 3; S. 556 - 562
Hauptverfasser: Wang, Zidong, Shen, Bo, Liu, Xiaohui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.03.2012
Elsevier
Schlagworte:
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Control system analysis
Control theory. Systems
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Missing measurements
Modelling and identification
Nonlinear systems
Random incomplete information
Randomly occurring sensor saturations
Regional [formula omitted] filters
Signal and communications theory
Signal, noise
Software
Telecommunications and information theory
Missing measurements
Randomly occurring sensor saturations
Nonlinear systems
Random incomplete information
Regional H∞ filters
Probabilistic approach
Saturation
Measurement sensor
Gain margin
H infinite control
Non linear control
Semi definite programming
Conditional probability
filters
Non linear system
Modeling
Convex programming
Asymptotic stability
Uncertain system
Regional H
Filter
System identification
Incomplete information
Sensor array
Ellipsoid
ISSN:0005-1098, 1873-2836
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Zusammenfassung:In this paper, the H∞ filtering problem is investigated for a class of nonlinear systems with randomly occurring incomplete information. The considered incomplete information includes both the sensor saturations and the missing measurements. A new phenomenon of sensor saturation, namely, randomly occurring sensor saturation (ROSS), is put forward in order to better reflect the reality in a networked environment such as sensor networks. A novel sensor model is then established to account for both the ROSS and missing measurement in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. Based on this sensor model, a regional H∞ filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean-square asymptotically stable and the H∞-norm requirement is satisfied. Note that the regional l2 gain filtering feature is specifically developed for the random saturation nonlinearity. The characterization of the desired filter gains is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite program method. Finally, a simulation example is employed to show the effectiveness of the filtering scheme proposed in this paper.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2012.01.008