Abstract
This paper addresses the problem of estimating the state for a class of uncertain discrete-time linear systems with constraints by using an optimization-based approach. The proposed scheme uses the moving horizon estimation philosophy together with the game theoretical approach to the H∞ filtering to obtain a robust filter with constraint handling. The used approach is constructive since the proposed moving horizon estimator (MHE) results from an approximation of a type of full information estimator for uncertain discrete-time linear systems, named in short H∞-MHE and H∞-full information estimator, respectively. Sufficient conditions for the stability of the H∞-MHE are discussed for a class of uncertain discrete-time linear systems with constraints. Finally, since the H∞-MHE needs the solution of a complex minimax optimization problem at each sampling time, we propose an approximation to relax the optimization problem and hence to obtain a feasible numerical solution of the proposed filter. Simulation results show the effectiveness of the robust filter proposed.
| Original language | English |
|---|---|
| Pages (from-to) | 326–341 |
| Journal | International Journal of Robust and Nonlinear Control |
| Volume | 28 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
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Keywords
- Constrained estimation
- Moving horizon estimation
- Optimization
- Robust estimation
- Uncertain linear systems
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A new approach to constrained state estimation for discrete-time linear systems with unknown inputs. / Garcia Tirado, J.F.; Marquez-Ruiz, A.; Botero Castro, H.; Angulo, F.
In: International Journal of Robust and Nonlinear Control, Vol. 28, No. 1, 01.01.2018, p. 326–341.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - A new approach to constrained state estimation for discrete-time linear systems with unknown inputs
AU - Garcia Tirado, J.F.
AU - Marquez-Ruiz, A.
AU - Botero Castro, H.
AU - Angulo, F.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This paper addresses the problem of estimating the state for a class of uncertain discrete-time linear systems with constraints by using an optimization-based approach. The proposed scheme uses the moving horizon estimation philosophy together with the game theoretical approach to the H∞ filtering to obtain a robust filter with constraint handling. The used approach is constructive since the proposed moving horizon estimator (MHE) results from an approximation of a type of full information estimator for uncertain discrete-time linear systems, named in short H∞-MHE and H∞-full information estimator, respectively. Sufficient conditions for the stability of the H∞-MHE are discussed for a class of uncertain discrete-time linear systems with constraints. Finally, since the H∞-MHE needs the solution of a complex minimax optimization problem at each sampling time, we propose an approximation to relax the optimization problem and hence to obtain a feasible numerical solution of the proposed filter. Simulation results show the effectiveness of the robust filter proposed.
AB - This paper addresses the problem of estimating the state for a class of uncertain discrete-time linear systems with constraints by using an optimization-based approach. The proposed scheme uses the moving horizon estimation philosophy together with the game theoretical approach to the H∞ filtering to obtain a robust filter with constraint handling. The used approach is constructive since the proposed moving horizon estimator (MHE) results from an approximation of a type of full information estimator for uncertain discrete-time linear systems, named in short H∞-MHE and H∞-full information estimator, respectively. Sufficient conditions for the stability of the H∞-MHE are discussed for a class of uncertain discrete-time linear systems with constraints. Finally, since the H∞-MHE needs the solution of a complex minimax optimization problem at each sampling time, we propose an approximation to relax the optimization problem and hence to obtain a feasible numerical solution of the proposed filter. Simulation results show the effectiveness of the robust filter proposed.
KW - Constrained estimation
KW - Moving horizon estimation
KW - Optimization
KW - Robust estimation
KW - Uncertain linear systems
UR - http://www.scopus.com/inward/record.url?scp=85026347493&partnerID=8YFLogxK
U2 - 10.1002/rnc.3874
DO - 10.1002/rnc.3874
M3 - Article
AN - SCOPUS:85026347493
VL - 28
SP - 326
EP - 341
JO - International Journal of Robust and Nonlinear Control
JF - International Journal of Robust and Nonlinear Control
SN - 1049-8923
IS - 1
ER -