A new approach to constrained state estimation for discrete-time linear systems with unknown inputs
| Autor: | Héctor Antonio Botero Castro, Alejandro Marquez-Ruiz, Fabiola Angulo, Jose Fernando Garcia Tirado |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization 021103 operations research Optimization problem Mechanical Engineering General Chemical Engineering Linear system 0211 other engineering and technologies Biomedical Engineering Stability (learning theory) Aerospace Engineering Estimator 02 engineering and technology Filter (signal processing) Minimax Constructive Industrial and Manufacturing Engineering Constraint (information theory) 020901 industrial engineering & automation Control and Systems Engineering Control theory Electrical and Electronic Engineering Mathematics |
| Zdroj: | International Journal of Robust and Nonlinear Control. 28:326-341 |
| ISSN: | 1049-8923 |
| DOI: | 10.1002/rnc.3874 |
| Popis: | Summary 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. |
| Databáze: | OpenAIRE |
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