Control Of A Bioprocess Using Orthonormal Basis Function Fuzzy Models

Fuzzy models within the framework of orthonormal basis functions (OBF Fuzzy Models) were introduced in previous works and have shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model architectures usually adopted in the literature. In the present paper these models are reviewed and used as a basis for a predictive control scheme which is applied to the control of a process for ethyl alcohol (ethanol) production.2801806Yager, R.R., Filev, D.P., (1994) Essentials of Fuzzy Modeling and Control, , John Wiley & SonsPedrycz, W., (1993) Fuzzy Control and Fuzzy Systems, , Research Studies Press/John Wiley & Sons, 2nd editionTakagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans. Systems, Man and Cybernetics, SMC-15, pp. 116-132Babuška, R., (1998) Fuzzy Modeling for Control, , Kluwer Academic PublishersCampello, R.J.G.B., Amaral, W.C., Modeling and linguistic knowledge extraction from systems using fuzzy relational models (2001) Fuzzy Sets and Systems, 121, pp. 113-126Campello, R.J.G.B., Amaral, W.C., Towards true linguistic modeling through optimal numerical solutions (2003) International Journal of Systems Science, 34, pp. 139-157Ninness, B., Gustafsson, F., A unifying construction of orthonormal bases for system identification (1997) IEEE Trans. on Automatic Control, 42, pp. 515-521Broome, P.W., Discrete orthonormal sequences (1965) Journal of the Association for Computing Machinery, 12 (2), pp. 151-168Dumont, G.A., Fu, Y., Non-linear adaptive control via Laguerre expansion of Volterra kernels (1993) Int. J. Adaptive Control and Signal Processing, 7, pp. 367-382Back, A.D., Tsoi, A.C., Nonlinear system identification using discrete Laguerre functions (1996) Journal of Systems Engineering, 6, pp. 194-207Oliveira, G.H.C., Amaral, W.C., Favier, G., Dumont, G.A., Constrained robust predictive controller for uncertain processes modeled by orthonormal series functions (2000) Automatica, 36 (4), pp. 563-571Campello, R.J.G.B., Meleiro, L.A.C., Amaral, W.C., Maciel Filho, R., Identification of a bioprocess using Laguerre function based models (2001) Proc. 6th World Congress on Chemical Engineering, pp. CD. , Melbourne/AustraliaOliveira, G.H.C., Campello, R.J.G.B., Amaral, W.C., Fuzzy models within orthonormal basis function framework (1999) Proc. 8th IEEE Internat. Conference on Fuzzy Svstems, pp. 957-962. , Seoul/KoreaFu, Y., Dumont, G.A., An optimum time scale for discrete Laguerre network (1993) IEEE Trans. on Automatic Control, 38 (6), pp. 934-938Campello, R.J.G.B., Favier, G., Amaral, W.C., Optimal expansions of discrete-time Volterra models using Laguerre functions Automatica, , (to appear), ndCampello, R.J.G.B., Amaral, W.C., Takagi-Sugeno fuzzy models within orthonormal basis function framework and their application to process control (2002) Proc. 11th IEEE Internat. Conference on Fuzzy Systems, pp. 1399-1404. , Honolulu/USAWahlberg, B., System identification using Kautz models (1994) IEEE Trans. on Automatic Control, 39 (6), pp. 1276-1282Boyd, S., Chua, L.O., Fading memory and the problem of approximating nonlinear operators with Volterra series (1985) IEEE Trans. on Circuits and Systems, 32 (11), pp. 1150-1161Ljung, L., (1999) System Identification: Theory for the User, , Prentice Hall, 2nd editionZervos, C.C., Dumont, G.A., Deterministic adaptive control based on Laguerre series representation (1988) Int. J. Control, 48 (6), pp. 2333-2359Wahlberg, B., Ljung, L., Hard frequency-domain model error bounds from least-squares like identification techniques (1992) IEEE Trans. on Automatic Control, 37 (7), pp. 900-912Camacho, E.F., Bordons, C., (1999) Model Predictive Control, , Springer-VerlagMeleiro, L.A.C., Filho, R.M., A self-tuning adaptive control applied to an industrial large-scale ethanol production (2000) Computers and Chemical Engineering, 24 (2-7), pp. 925-930Andrietta, S.R., Maugeri, F., Optimum design of a continuous fermentation unit of an industrial plant for alcohol production (1994) Advances in Bioprocess Engineering, pp. 47-52. , E. Galindo and O. T. Ramirez, Eds., Kluwer Academic PublishersMeleiro, L.A.C., Filho, R.M., Campello, R.J.G.B., Amaral, W.C., Hierarchical neural fuzzy models as a tool for process identification: A bioprocess application (2001) Application of Neural Networks and Other Learning Technologies in Process Engineering, , I. M. Mujtaba and M. A. Hussain, Eds. Imperial College PressJang, J.-S.R., ANFIS: Adaptive-network-based fuzzy inference system (1993) IEEE Trans. Systems, Man and Cybernetics, 23, pp. 665-685Setnes, M., Supervised fuzzy clustering for rule extraction (2000) IEEE Trans. Fuzzy Systems, 8, pp. 416-424Delgado, M.R., Von Zuben, F.J., Gomide, F.A.C., Evolutionary design of Takagi-Sugeno fuzzy systems: A modular and hierarchical approach (2000) Proc. 9th IEEE Internat. Conference on Fuzzy Systems, pp. 447-452. , Houston/USAGüven, M.K., Passino, K.M., Avoiding exponential parameter growth in fuzzy systems (2001) IEEE Trans. Fuzzy Systems, 9, pp. 194-19