<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The problem of estimation of unmeasured state variables and unknown reaction kinetic functions for selected biochemical processes modelled as a continuous stirred tank reactor is addressed in this paper. In particular, a new hierarchical (sequential) state observer is derived to generate stable and robust estimates of the state variables and kinetic functions. The developed hierarchical observer uses an adjusted asymptotic observer and an adopted super-twisting sliding mode observer. The stability of the proposed hierarchical observer is investigated under uncertainty in the system dynamics. The stability analysis of the estimation error dynamics is carried out based on the methodology associated with linear parameter-varying systems and sliding mode regimes. The developed hierarchical observer is implemented in the Matlab/Simulink environment and its performance is validated via simulation. The obtained satisfactory estimation results demonstrate high effectiveness of the devised hierarchical observer.</p>
</abstract>

The problem of estimation of unmeasured state variables and unknown reaction kinetic functions for selected biochemical processes modelled as a continuous stirred tank reactor is addressed in this paper. In particular, a new hierarchical (sequential) state observer is derived to generate stable and robust estimates of the state variables and kinetic functions. The developed hierarchical observer uses an adjusted asymptotic observer and an adopted super-twisting sliding mode observer. The stability of the proposed hierarchical observer is investigated under uncertainty in the system dynamics. The stability analysis of the estimation error dynamics is carried out based on the methodology associated with linear parameter-varying systems and sliding mode regimes. The developed hierarchical observer is implemented in the Matlab/Simulink environment and its performance is validated via simulation. The obtained satisfactory estimation results demonstrate high effectiveness of the devised hierarchical observer.

A Hierarchical Observer for a Non–Linear Uncertain CSTR Model of Biochemical Processes

Department of Intelligent Control and Decision Support Systems

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The problem of estimation of unmeasured state variables and unknown reaction kinetic functions for selected biochemical processes modelled as a continuous...

A Hierarchical Observer for a Non–Linear Uncertain CSTR Model of Biochemical Processes

Online veröffentlicht: 26. März 2024

Seitenbereich: 45 - 64

Eingereicht: 22. Aug. 2023

Akzeptiert: 08. Dez. 2023

DOI: https://doi.org/10.61822/amcs-2024-0004

Schlüsselwörter
bioreactors, hierarchical observers, non-linear uncertain systems, observers, state estimation

© 2024 Mateusz Czyżniewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Hierarchical Observer for a Non–Linear Uncertain CSTR Model of Biochemical Processes

Online veröffentlicht: 26. März 2024

Seitenbereich: 45 - 64

Eingereicht: 22. Aug. 2023

Akzeptiert: 08. Dez. 2023

DOI: https://doi.org/10.61822/amcs-2024-0004

Schlüsselwörterbioreactors, hierarchical observers, non-linear uncertain systems, observers, state estimation

© 2024 Mateusz Czyżniewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
bioreactors, hierarchical observers, non-linear uncertain systems, observers, state estimation