scholarly journals Nonlinear predictors for systems with bounded trajectories and delayed measurements

Automatica ◽  
2015 ◽  
Vol 59 ◽  
pp. 127-138 ◽  
Author(s):  
Stefano Battilotti
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sumanta Kumar Nanda ◽  
Guddu Kumar ◽  
Vimal Bhatia ◽  
Abhinoy Kumar Singh

1994 ◽  
Vol 35 (6) ◽  
pp. 2914-2933 ◽  
Author(s):  
Toshihiro Iwai ◽  
Noriaki Katayama

1999 ◽  
Vol 5 (2) ◽  
pp. 121-137 ◽  
Author(s):  
Magdi S. Mahmoud ◽  
Mohamed Zribi

In this paper, the problem of designing observers and observer-based controllers for a class of uncertain systems with input and state time lags is considered. We construct delay-type observers in which both the instantaneous as well as the delayed measurements are utilized. Using feedback control based on the reconstructed state, the behavior of the closed-loop system is investigated. It is established that the uncertain time-lag system with delay observer-based control is asymptotically stable. Expressions for the gain matrices are given based on two linear-matrix inequalities. A numerical example is given to illustrate the theoretical developments.


1988 ◽  
Vol 108 (3-4) ◽  
pp. 371-378
Author(s):  
B. M. Garay

SynopsisIn the Banach space of real sequences which converge to zero with the supremum norm, we construct a parallelisable dynamical system with uniformly-bounded trajectories.


2009 ◽  
Vol 51 (2) ◽  
pp. 218-233 ◽  
Author(s):  
CHUNYAN HAN ◽  
HUANSHUI ZHANG

AbstractThis paper investigates the linear minimum mean-square error estimation for discrete-time Markovian jump linear systems with delayed measurements. The key technique applied for treating the measurement delay is reorganization innovation analysis, by which the state estimation with delayed measurements is transformed into a standard linear mean-square filter of an associated delay-free system. The optimal filter is derived based on the innovation analysis method together with geometric arguments in an appropriate Hilbert space. The solution is given in terms of two Riccati difference equations. Finally, a simulation example is presented to illustrate the efficiency of the proposed method.


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