Actas de congresos
Crhpc Using Volterra Models And Orthonormal Basis Functions: An Application To Cstr Plants
Registro en:
Ieee Conference On Control Applications - Proceedings. , v. 1, n. , p. 718 - 723, 2003.
2-s2.0-0344121594
Autor
Oliveira G.H.C.
Amaral W.C.
Latawiec K.
Institución
Resumen
This work is focused on predictive control of non-linear systems modelled by Volterra series with orthonormal basis functions expansion (Volterra-OBF). By using Volterra series, any non-linear analytical operator with finite memory can be approximated with an arbitrary precision. A Laguerre basis expansion is then used in the model parameterization to reduce the number of coefficients of the Volterra model. In this context, a predictive control with terminal constraints window based on cited model is proposed. Finally, simulated results using a CSTR unit illustrates the control scheme performance. 1
718 723 Clarke, D.W., (1994) Advances in Model Based Predictive Control, , D. W. Clarke, Ed. Oxford University Press Allgower, F., Zheng, A., (2000) Nonlinear Model Predictive Control, Ser. Progress in Systems and Control Theory, , F. Allgower and A. Zheng, Eds. Birkhauser Verlag, Switzerland Billings, S.A., Identification of nonlinear systems - a survey (1980) IEE Proc. Pt D, 127 (6), pp. 272-285 Doyle, F.J., Ogunnaike, B.A., Pearson, R.K., Nonlinear model-based control using second-order Volterra models (1995) Automatica, 31 (5), pp. 697-714 Maner, B.R.F.J.D., Ogunnaike, B.A., Pearson, R.K., Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models (1996) Automatica, 32 (9), pp. 1285-1301 Wu, F., LMI-based robust model predictive control evaluated on an industrial CSTR model Proc of the IEEE International Conference on Control Applications, Hartford/CT, USA, 1997, pp. 609-614 Nagrath, D., Prasad, V., Bequette, B.W., Model predictive control of open-loop unstable cascade systems Proc. of the American Control Conference, Chicago/Illinois, USA, June 2000, pp. 3747-3752 Schetzen, M., (1980) The Volterra and Wiener Theories of Nonlinear Systems, , John Wiley & Sons Clarke, D.W., Scattolini, R., Constrained receding horizon predictive control (1991) IEE Proc. D, 138 (4), pp. 347-354. , july Mayne, D.Q., Michalska, H., Receding horizon control of nonlinear systems (1990) IEEE Trans. on Automatic Control, 35 (7), pp. 814-824 Rawlings, J.B., Muske, K.R., The stability of constrained receding horizon control (1993) IEEE Transaction on Automatic Control, 38 (10), pp. 1512-1516 Michalska, H., Mayne, D.Q., Robust receding horizon control of constrained nonlinear systems (1993) IEEE Trans. on Automatic Control, 38 (11), pp. 1623-1633 Boyd, 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. 1150-1161 Ninness, B., Gustafsson, F., Orthonormal bases for system identification (1995) Proc. of 3rd European Control Conference, 1, pp. 13-18. , Roma/Italy, September Oliveira, G.H.C., Campello, R.G.L., Amaral, W.C., Fuzzy models within orthonormal basis functions (1999) 8-th IEEE International Conference on Fuzzy Systems, 2, pp. 957-962. , Seoul/Korea Sentoni, G., Agamennoni, O., Desages, A., Romagnoli, J., Approximate models for nonlinear process control (1996) AIChE Journal, 42, pp. 2240-2250 Lindskog, P., Methods, algorithms and tools for system identification based on prior knowledge (1996), Ph.D. dissertation, Linkoping University - SwedenWahlberg, B., Makila, P.M., On approximation of stable linear dynamical systems using Laguerre and Kautz functions (1996) Automatica, 32 (5), pp. 693-708 Ninness, B., Gustafsson, F., A unifying construction of orthonormal bases for system identification Proc. of the CDC, Orlando, Florida/USA, 1994, pp. 3388-3393 Den Hof, P.M.J.V., Hueberger, P.S.C., Bokor, J., System identification with generalized orthonormal basis functions (1995) Automatica, 31 (12), pp. 1821-1834 Oliveira, G.H.C., Amaral, W.C., Favier, G., Dumont, G., Constrained robust predictive controller for uncertain processes modeled by orthonormal series functions (2000) Automatica, 36 (4), pp. 563-572 Makila, P.M., Approximation of stable systems by Laguerre filters (1990) Automatica, 26 (2), pp. 333-345 Zervos, C.C., Dumont, G.A., Deterministic adaptive control based on Laguerre series representation (1988) International Journal of Control, 48 (6), pp. 2333-2359 Wahlberg, B., Ljung, L., Hard frequency-domain model error bounds from least-squares like identification techniques (1992) IEEE Trans. on Automatic Control, 37 (7), pp. 900-912. , July Fu, Y., Dumont, G.A., An optimum time scale for discrete Laguerre network (1993) IEEE Transactions on Automatic Control, 38 (6), pp. 934-938. , june Olivier, P.D., Online system identification using Laguerre series (1994) IEE Proceedings-D, 141 (4), pp. 249-254. , july Silva, T.O., On the determination of the optimal pole position of Laguerre filters (1995) IEEE Trans. on Signal Processing, 43 (9), pp. 2079-2087 Mosca, E., Zhang, J., Stable redesign of predictive control (1992) Automatica, 28 (6), pp. 1229-1233 Demircioglu, H., Clarke, D.W., Generalized predictive control with end-point state weighting (1993) IEE Proceedings-D, 140 (4), pp. 275-282. , july Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M., Constrained model predictive control: Stability and optimality (2000) Automatica, 36, pp. 789-814 Morari, M., Zafiriou, E., (1989) Robust Process Control, , Prentice Hall Inc Narendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Transactions on Neural Networks, 1 (1), pp. 4-27