In this article, we propose a Moving-Horizon State-Estimation method, applied to a neural dynamical process model. Firstly, the approach chosen to represent a nonlinear dynamical system by a neural network is explained. After that, the MHSE method, used to perform the state estimation, is presented. The algorithm performances are showed on a biotechnological process. The combination of the MHSE method and the neural network permits a particularly efficient estimation of the state of the process. with a nonlinear model easy to build thanks to the neural network, and with an easy tuning due to the choice of the MHSE method.
Aeyels, D.1981. 'On the number of samples necessary to achieve observability' . Systems Control Lett, 1, 92-94.
2.
Anon.1984. VA10AD, UKAEA, Harwell.
3.
Boillereaux, L.1996. 'Estimation d'état de procédés nonlinéaires: méthode à horizon glissant avec indicateur de qualité'. PhD thesis, L.A.G.-I.N.P.Grenoble.
4.
Boillereaux, L., and Flaus, J.M.1995a. 'Adaptive receding horizon state estimation for non linear processes'. Proceedings 5th IFAC Symposium on Adaptive Systems in Control and Signal Processing, Budapest, Hungary, June, 147-152.
5.
Boillereaux, L., and Flaus, J.M.1995b. 'A new algorithm for real time adaptive state estimation '. Proceedings 3rd IFAC/IFIP Workshop on Algorithms and Architectures for Real Time Control. Ostend, Belgium, June, 219-224.
6.
Cheruy, A., and Flaus, J.M.1994. Des mesures indirectes à l'estimation en ligne, capteurs et mesures en biotechnologie, chapter 12, pages 444-484. Lavoisier .
7.
Derese, I., Stevens, P., and Noldus, E.1979. 'Observers for bilinear systems with bounded input' , Int Journal of Systems Science, 10(6), 649-668.
8.
Flaus, J.M.1994. La régulation industrielle: régulateurs PID, prédictifs et flous. Hermes.
9.
Gauthier, J.P., Hammouri, H., and Othman, S.1992. 'A simple observer for non-linear systems applications to bioreactors', IEEE Trans on Automatic Control.
10.
Hopfield, J.J.1982. 'Neural networks and physical systems with emergent collective commutational abilities', Proceedings of the National Academy of Sciences, USA, 79, 2554—2558.
11.
Hornik, K., Stinchcombe, M., and White, H.1988. 'Multilayer feedforward networks are universal approximators' . Neural Networks, 2, 359-366.
12.
Hunt, K.J., Sbarbaro, D., Zbikowski, R., and Gawthrop, P.J.1992. 'Neural networks for control systems- a survey' . Automatica, 28(6), 1083-1112.
13.
Jazwinski, A.H.1970. Stochastic Processes and Filtering Theory, vol 64. Mathematics in Science and Engineering, Academic Press .
14.
Kalman, R.E., and Bucy, R.S.1961. 'New results in linear filtering and prediction theory' . Trans ASME, 83(D), 95-108.
15.
Lawson, C.L., and Hanson, R.J.1974. Solving Least Squares Problems. Prentice Hall, Inc, Englewood Cliffs, NJ.
16.
Luenberger, D.G.1971. 'An introduction to observers', IEEE Trans Automatic Control, AC-16, 596-602.
17.
McCulloch, W.S., and Pitts, W.1943. A logical calculus of the ideas imminent in nervous activity' . Bulletin of Mathematical Biophysics, 5, 115-133.
18.
Othman, S.1992. 'Sur les observateurs des systemes non lineaires' . PhD thesis, Universite Claude Bernard, LyonI.
19.
Thibault, J., Van Breusegem, V., and Cheruy, A.1990. 'On-line prediction of fermentation variables using neural networks'. Biotechnology and Bioengineering, 36, 1041-1048.
20.
Walter, E., and Pronzato, L.1994. Identification de modèles paramétriques à partir de données expérimentales. Masson.
21.
You, Y., and Nikolaou, M.1993. 'Dynamic process modeling with recurrent neural networks' . AIChE Journal, 39(10), 1654-1667.
