Algorithm for adaptive observation based on method of instrumental variables

When the input signal and the output value of the object of control cannot be measured accurately, the state vector is estimated. The instrumental variables (IVs) method is a commonly used parameter estimation method [1-10]. The task of adaptive observation is to create state observers containing pa...

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Veröffentlicht in:E3S web of conferences Jg. 401; S. 4003
Hauptverfasser: Nikolov, Nikola, Alexandrova, Mariela, Ubaydullayeva, Shakhnoza, Gaziyeva, Rano, Zulfiya, Khafizova, Ubaydullayeva, Dilorom
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: Les Ulis EDP Sciences 01.01.2023
Schlagworte:
Adaptive algorithms
Algorithms
Discrete systems
Matrices (mathematics)
Parameter estimation
State observers
State space models
State vectors
Workability
ISSN:2267-1242, 2555-0403, 2267-1242
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Zusammenfassung:When the input signal and the output value of the object of control cannot be measured accurately, the state vector is estimated. The instrumental variables (IVs) method is a commonly used parameter estimation method [1-10]. The task of adaptive observation is to create state observers containing parameter estimators. In adaptive observers, the matrices A and b or c (depending on the chosen canonical state-space representation form) are assumed to be unknown. In the monitoring process, parameter estimation is performed, the unknown matrices are determined, and then the state vector is calculated. The paper aims to present a non-recurrent adaptive observation algorithm for SISO linear time-invariant (LTI) discrete systems. The algorithm is based on the instrumental variables (IVs) method, and the adaptive state observer (ASO) estimates the parameters, the initial and the current state vectors of the discrete system. The algorithm's workability and effectiveness are proved by using simulation data in MATLAB/Simulink.
Bibliographie:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202340104003