Reza López, Víctor Alejandro | CINVESTAV-IPN |
Torres Muñoz, J.A. | CINVESTAV-IPN |
Guerrero, Jesus | Tecnológico Nacional de México |
https://doi.org/10.58571/CNCA.AMCA.2023.023
Resumen: The monitoring and control of bioreactors is essential for any bioprocess. Therefore, measuring some essential biochemical key variables is crucial, such as the reaction rates, whose structure is usually barely known. Although many high gain and sliding mode observers-based estimators were designed to estimate these variables, these strategies are not robust against external disturbances. This work extends the high gain and sliding mode techniques to simultaneously estimate the reaction rates and an external disturbance under a persistent excitation condition that is easy to verify. Furthermore, the proposed algorithms are exponentially and finite-time stable, respectively. Finally, both observers are tested in a continuous fermentation process simulation.
![Application of High Gain and Sliding Mode Observers for Estimating Key Variables in Bioprocesses](/wp-content/uploads/2022/04/logotipo_amca100.png)
¿Cómo citar?
Reza López, Víctor Alejandro; Torres Muñoz, J.A.; Guerrero, Jesus. Application of High Gain and Sliding Mode Observers for Estimating Key Variables in Bioprocesses. Memorias del Congreso Nacional de Control Automático, pp. 56-61, 2023. https://doi.org/10.58571/CNCA.AMCA.2023.023
Palabras clave
Procesos Biotecnológicos; Control de Sistemas No Lineales; Control de Procesos
Referencias
- Alcaraz-Gonzalez, V. and Gonzalez-Alvarez, V. (2007). Robust nonlinear observers for bioprocesses: Application to wastewater treatment. In Selected topics in dynamics and control of chemical and biological processes, 119-164. Springer.
- Bastin, G. (2013). On-line estimation and adaptive control of bioreactors, volume 1. Elsevier.
- Bouraoui, I., Farza, M., M´enard, T., Abdennour, R.B., M’Saad, M., and Mosrati, H. (2015). Observer design for a class of uncertain nonlinear systems with sampled outputs application to the estimation of kinetic rates in bioreactors. Automatica, 55, 78-87. C
- elikovsk`y, S., Torres-Munoz, J.A., and Dominguez-Bocanegra, A.R. (2018). Adaptive high gain observer extension and its application to bioprocess monitoring. Kybernetika, 54(1), 155-174.
- Darvehei, P., Bahri, P.A., and Moheimani, N.R. (2018). Model development for the growth of microalgae: A review. Renewable and Sustainable Energy Reviews, 97, 233-258.
- De Battista, H., Pic´o, J., Garelli, F., and Vignoni, A. (2011). Specific growth rate estimation in (fed-) batch bioreactors using second-order sliding observers. Journal of Process Control, 21(7), 1049-1055.
- Farza, M., M’Saad, M., Fall, M.L., Pigeon, E., Gehan, O., and Busawon, K. (2013). Continuous discrete time observers for a class of mimo nonlinear systems. IEEE Transactions on Automatic Control, 59(4), 1060-1065.
- Huang, Q., Jiang, F., Wang, L., and Yang, C. (2017). Design of photobioreactors for mass cultivation of photosynthetic organisms. Engineering, 3(3), 318-329.
- Liu, F., Farza, M., and M’Saad, M. (2006). Unknown input observers design for a class of nonlinear systemsapplication to biochemical processes. IFAC Proceedings Volumes, 39(9), 131-136.
- Lofberg, J. (2004). Yalmip: A toolbox for modeling and optimization in matlab. In 2004 IEEE international conference on robotics and automation (IEEE Cat. No. 04CH37508), 284-289. IEEE.
- Louren¸co, N., Lopes, J., Almeida, C., Sarragu¸ca, M., and Pinheiro, H. (2012). Bioreactor monitoring with spectroscopy and chemometrics: a review. Analytical and bioanalytical chemistry, 404(4), 1211-1237.
- Moreno, J.A. and Dochain, D. (2008). Global observability and detectability analysis of uncertain reaction systems and observer design. International Journal of Control, 81(7), 1062-1070.
- Moreno, J.A. and Mendoza, I. (2014). Application of super-twisting-like observers for bioprocesses. In 2014 13th International Workshop on Variable Structure Systems (VSS), 1-6. IEEE.
- Moreno, J.A., Rocha-C´ozatl, E., and Wouwer, A.V. (2014). A dynamical interpretation of strong observability and detectability concepts for nonlinear systems with unknown inputs: application to biochemical processes. Bioprocess and biosystems engineering, 37(1), 37-49.
- Nuñez, S., De Battista, H., Garelli, F., Vignoni, A., and Pic´o, J. (2013). Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses. Control Engineering Practice, 21(9), 1259-1265.
- Perrier, M., De Azevedo, S.F., Ferreira, E., and Dochain, D. (2000). Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic Control Engineering Practice, 8(4), 377-388.
- Picó-Marco, E., Picó*, J., and De Battista, H. (2005). Sliding mode scheme for adaptive specific growth rate control in biotechnological fed-batch processes. International Journal of Control, 78(2), 128-141.
- Rapaport, A. and Dochain, D. (2005). Interval observers for biochemical processes with uncertain kinetics and inputs. Mathematical biosciences, 193(2), 235-253.
- Reza López, V., Guerrero Tavares, J., and Torres Muñoz, J. (2023). An extended supertwisting algorithm for simultaneous estimation of reaction rates and input disturbance in bioprocess. Journal of Process Control, 123, 131-140.
- Robles-Magdaleno, J., Rodr´ıguez-Mata, A., Farza, M., and M’saad, M. (2020). A filtered high gain observer for a class of non uniformly observable systems–application to a phytoplanktonic growth model. Journal of Process Control, 87, 68-78.
- Rocha-Cozatl, E., Moreno, J.A., and Wouwer, A.V. (2012). Application of a continuous-discrete unknown input observer to estimation in phytoplanktonic cultures. IFAC Proceedings Volumes, 45(15), 579-584.
- Sturm, J.F. (1999). Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones. Optimization methods and software, 11(1-4), 625-653.
- Utkin, V., Poznyak, A., Orlov, Y.V., and Polyakov, A. (2020). Road map for sliding mode control design.
- Zúñiga, I.T., Vargas, A., Latrille, E., and Buitrón, G. (2015). Robust observation strategy to estimate the substrate concentration in the influent of a fermentative bioreactor for hydrogen production. Chemical Engineering Science, 129, 126-134.