Stochastic linear retarded systems: Robust vertex-dependent H∞ control via measurement-feedback

2017 ◽  
Author(s):  
Eli Gershon ◽  
Uri Shaked
Keyword(s):  
Retarded Systems ◽  
Measurement Feedback

Hθ Measurement-feedback Control for Systems with Input and Measurement Delays

2009 ◽  
Vol 34 (11) ◽  
pp. 1417-1423
Author(s):  
Hong-Xia WANG ◽  
Huan-Shui ZHANG
Keyword(s):  
Feedback Control ◽  
Measurement Feedback

The discrete-time H/sub ∞/ control problem with strictly proper measurement feedback

1994 ◽  
Vol 39 (9) ◽  
pp. 1936-1939 ◽  
Author(s):  
A.A. Stoorvogel ◽  
A. Saberi ◽  
B.M. Chen
Keyword(s):  
Control Problem ◽  
Discrete Time ◽  
Measurement Feedback

scholarly journals Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Xin Wang ◽  
Shu-Li Sun
Keyword(s):  
Kalman Filter ◽  
Stochastic Systems ◽  
Tracking System ◽  
Weighted Least Squares ◽  
Full Rank ◽  
Global Optimality ◽  
Measurement Feedback ◽  
Innovation Model ◽  
Self Tuning ◽  
Noise Statistics

For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory. Further, a self-tuning weighted measurement fusion Kalman filter is presented. The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics. The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics. It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality. The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.

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