Bogaerts, Ph. and Vande Wouwer, A., Parameter identification of a model to be used for state estimation - Application to a bioprocess. Proceedings of the IEE Control 2000 Conference, Cambridge, 2000.
Bogaerts, Ph. Vande Wouwer, A., Parameter identification for state estimation of a fed-batch bioreactor: Analysis through model falsification. Proceedings of the 8th IFAC Conference on Computer Applications in Biotechnology (CAB8), Montréal, 2001, pp. 436-441.
Bastin, G. and Dochain, D., On-line Estimation and Adaptive Control of Bioreactors. Elsevier, Amsterdam, 1990.
Gauthier, J. P., Hammouri, H., and Othman, S., A simple observer for nonlinear systems - Application to bioreactors. IEEE Trans. Autom. Control 37, 875-880 (1992).
Chéruy, A., Software sensors in bioprocess engineering. J. Biotechnol. 52, 193-199 (1997).
Gouzé, J.-L., Rapaport, A., and Hadj-Sadok, M. Z., Interval observers for uncertain biological systems. Ecol. Modell. 133, 45-56 (2000).
José de Assis, A. and Filho, R. M., Soft sensors development for on-line bioreactor state estimation. Comput. Chem. Eng. 24, 1099-1103 (2000).
Keesman, K. J., State and parameter estimation in biotechnical batch reactors. Control Eng. Pract. 10, 219-225 (2002).
Oliveira, R., Ferreira, E. C., and Feyo de Azevedo, S., Stability, dynamics of convergence and tuning of observer-based kinetics estimators. J. Process Control 12, 311-323 (2002).
Ryckaert, V. G., Jacobs, E., and Van Impe, J. F., Robustness of nonlinear observers - Application to biological models. Proceedings of the European Control Conference (ECC' 97), Brussels, 1997, pp. 665-670.
Kwakernaak, H. and Sivan, R., Linear Optimal Control Systems. John Wiley & Sons, New York, 1972.
Zeitz, M., The extended Luenberger observer for nonlinear systems. Syst. Control Lett. 9, 149-156 (1987).
Gelb, A., Applied Optimal Estimation. MIT Press, Cambridge, 1976.
Allgöwer, F., Bagdwell, T. A., Qin, J. S., Rawlings, J. B., and Wright, S. J., Nonlinear predictive control and moving horizon estimation - A introduction overview. In Advances in Control (Highlights of ECC'99), edited by P. M. Frank. Springer-Verlag, Berlin, 1999, pp. 391-449.
Bogaerts, Ph. and Hanus, R., On-line state estimation of bioprocesses with full horizon observers. Math. Comput. Simul. 56, 425-441 (2001).
Jang, S. S., Joseph, B., and Muka, H., Comparison of two approaches to on-line parameter and state estimation of nonlinear systems. Ind. Eng. Chem. Process Des. Dev. 25, 809-813 (1986).
Cadet, C., Touré, Y., Gilles, G., and Gatina, J. C., Observation and nonlinear predictive control of evaporators for a cane sugar production plant. In Proceedings of the IMACS Multiconference on Computational Engineering in Systems Applications (CESA '98), Vol. 1, Nabeuel-Hammamet, 1998, pp. 353-358.
Holmberg, A., On the practical identifiability of microbial growth models incorporating Michaelis-Menten type nonlinearities. Math. Biosci. 62, 23-43 (1982).
Ajinkja, M. B., Ray, W. H., Yu, T. K., and Seinfeld, J. H., The application of an approximate nonlinear filter to systems governed by coupled ordinary and partial differential equations. Int. J. Syst. Sci. 6, 313-332 (1975).
Vande Wouwer, A. and Zeitz, M., State estimation in distributed parameter systems, in Control Systems, Robotics and Automation, edited by H. Unbehauen, Encyclopedia of Life Support Systems, EOLSS Publishers, Oxford, UK, 2003.
Dochain, D., State observers for tubular reactors with unknown kinetics. J. Process Control 10, 259-268 (2000).