scholarly journals Design of a Common Observer for Two Linear Systems with Unknown Inputs

2006 ◽  
Author(s):  
H. Trinh ◽  
T. Fernando
Keyword(s):  
Linear Systems ◽  
Unknown Inputs

scholarly journals A new approach to constrained state estimation for discrete-time linear systems with unknown inputs

2017 ◽  
Vol 28 (1) ◽  
pp. 326-341 ◽  
Author(s):  
Jose Fernando Garcia Tirado ◽  
Alejandro Marquez-Ruiz ◽  
Hector Botero Castro ◽  
Fabiola Angulo
Keyword(s):  
State Estimation ◽  
Linear Systems ◽  
Discrete Time ◽  
New Approach ◽  
Unknown Inputs ◽  
Time Linear

Partial state observers for linear systems with unknown inputs

Automatica ◽  
2008 ◽  
Vol 44 (12) ◽  
pp. 3126-3132 ◽  
Author(s):  
Shreyas Sundaram ◽  
Christoforos N. Hadjicostis
Keyword(s):  
Linear Systems ◽  
Unknown Inputs ◽  
State Observers ◽  
Partial State

DESIGN OF REDUCED-ORDER FUNCTIONAL OBSERVERS FOR LINEAR SYSTEMS WITH UNKNOWN INPUTS

2008 ◽  
Vol 6 (4) ◽  
pp. 514-520 ◽  
Author(s):  
H. Trinh ◽  
T. Fernando ◽  
S. Nahavandi
Keyword(s):  
Linear Systems ◽  
Reduced Order ◽  
Unknown Inputs

scholarly journals High-order sliding modes observer for linear systems with unbounded unknown inputs

2009 ◽  
Vol 42 (17) ◽  
pp. 216-221 ◽  
Author(s):  
Leonid Fridman ◽  
Arie Levant ◽  
Jorge Davila
Keyword(s):  
Linear Systems ◽  
High Order ◽  
Sliding Modes ◽  
Unknown Inputs

Design of observers for linear systems with unknown inputs

1992 ◽  
Vol 37 (6) ◽  
pp. 871-875 ◽  
Author(s):  
M. Hou ◽  
P.C. Muller
Keyword(s):  
Linear Systems ◽  
Unknown Inputs

Filtering of Linear Systems With Unknown Inputs

2003 ◽  
Vol 125 (3) ◽  
pp. 482-485 ◽  
Author(s):  
Hosam E. Emara-Shabaik
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Linear Systems ◽  
Gaussian Noise ◽  
Unknown Inputs ◽  
Fast Tracking ◽  
Disturbance Estimation ◽  
Simulation Results ◽  
System States ◽  
Unknown Disturbance

State estimation of linear systems under the influence of both unknown deterministic inputs as well as Gaussian noise is considered. A Kalman like filter is developed which does not require the estimation of the unknown inputs as is customarily practiced. Therefore, the developed filter has reduced computational requirements. Comparative simulation results, under the influence of various types of unknown disturbance inputs, show the merits of the developed filter with respect to a conventional Kalman filter using disturbance estimation. It is found that the developed filter enjoys several practical advantages in terms of accuracy and fast tracking of the system states.

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