Control IMC No Lineal Tolerante a Fallos

Sergio Saludes Rodil, M. J. Fuente


Este artículo trata sobre el control IMC no lineal y un método para hacerlo tolerante a los fallos en la planta. El control IMC no lineal se consigue por medio de modelos no lineales de la planta y de la inversa de la dinámica de la misma. Ambos se hacen mediante redes neuro-difusas del tipo ANFIS. La tolerancia a los fallos abruptos e incipientes en la planta se consigue mediante la adición de una señal de control compensadora. Ésta se calcula mediante una red neuronal que se entrena en línea a partir de la minimización del error de control. Se muestran resultados en simulación para una planta de control de pH.

Palabras clave

IMC; redes neuronales; control no lineal; control tolerante a fallos

Texto completo:



Boukezzoula, Reda, Sylvie Galichet and Laurent Foulloy (2003). Nonlinear internal model control: Application of inverse model based fuzzy control. IEEE Transactions on Fuzzy Systems 11(6), 814–829.

Chen, J., Ron J. Patton and Z. chen (1999). Active fault tolerant flight control systems design using the linear matrix inequality method. Transactions of the Institute of Measurement Control 21(2/3), 77–84.

Cui, Xianzhong and Kang G. Shin (1993). Direct control and coordination using neural networks. IEEE Transactions on Systems, Man and Cybernetics 23(3), 686–697.

de Prada, César, Pastora Vega and Luis Alonso (1984). Modelling and simulation of a sulfitation tower for adaptive control. In: 11th IASTED conference Applied Modelling and Simulation.

Economou, C. G., Manfred Morari and B. O. Palsson (1986). Internal model control. 5. extension to nonlinear systems. Industrial Engineering Chemical Process Design and Development 25, 403–411.

Edgar, Craig R. and Bruce E. Postlethwaite (1999). Using fuzzy relational models for control. In: Prceedings of the European Sysmposium on Intelligent Techniques. Orthodox Academy of Crete, Grecia.

Fink, Alexander, Oliver Nelles and Rolf Isermann (2002). Nonlinear internal model control for MISO systems based on local linear neuro– fuzzy models. In: Proceedings of the 15th IFAC World Congress. Barcelona.

Fuente, M. J., G. I. Sáinz, M. Alonso and A. Aguado (2005). Neuro–fuzzy control of a pH plant. In: Proceedings of the 16th IFAC World Congress. Praga.

Gao, Z. and P. J. Antsaklis (1991). Stability of the pseudo–inverse method for reconfigurable control systems. International Journal Control 53, 717–729.

García, C. E. and Manfred Morari (1982). Internal model control – 1. a unifiying review and some new results. Ind. Eng. Chem. Process Des. Dev. 21, 308–323.

Haykin, Simon (1999). Neural Networks: a Comprehensive Foundation. 2a ed. Prentice Hall International, Inc.

Hirschorn, Ronald M. (1979). Invertibility of multivariable nonlinear control systems. IEEE Transactions on Automatic Control 24(6), 855–865.

Hunt, K. J. and D. Sbarbaro (1991). Neural networks for nonlinear internal model control. In: IEE Proceedings–D (IEE, Ed.). Vol. 138. IEE. pp. 431–438.

Hunt, K. J., D. Sbarbaro, R. Zbikowski and P. J. Gawthrop (1992). Neural networks for control systems – a survey. Automatica 28(6), 1083– 1112.

Jang, J.R. (1993). ANFIS: Adaptative-networkbased fuzzy inference system. IEEE Trans. on Systems, Man and Cybernetics 23, 665–685.

Jiang, J. (1994). Design of reconfigurable control systems using eigenstructure assignments. International Journal of Control 59(2), 395– 410.

Jordan, Michael I. and Davis E. Rumelhart (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science 16, 307–354.

Kaminer, I., A.M. Pascoal, P.P. Khargonekar and E.E. Coleman (1995). A velocity algorithm for the implementation of gain–scheduled controllers. Automatica 31(8), 1185–1192.

Linden, A. and J. Kinndermann (1989). Inversion of multilayer nets. In: Proceedings of the International Joint Conference on Neural Networks. Vol. 2. pp. 425 – 430.

Looze, D. P., J. L. Weiss, J. S. Eterno and N. M. Barret (1985). An automatic redesign approach for restructurable control systems. IEEE Control Systems Magazine 5(2), 16–22.

Morari, Manfred and Evanghelos Zafirou (1989). Robust Process Control. Prentice–Hall.

Morse, W.D. and K.A. Ossman (1990). Model– following reconfigurable flight control system for the AFTI/F–16. Journal of Guidance, Control and Dynamics 13(6), 969–976.

Noriega, Jose R. and H. Wang (1998a). A heuristic approach to fault tolerant control of unknown nonlinear systems using neural networks. In: Proceedings of Fifth IFAC Workshop on Algorithms and architectures for real–time control. Canc´un, M´exico. pp. 265–269.

Noriega, Jose R. and Hong Wang (1998b). A direct adaptive neural–network control for unknown nonlinear systems and its application. IEEE Transactions on Neural Networks 9(1), 27– 34.

Ochi, Y. (1993). Application of feedback linearisation method in a digital restructurable flight control system. Journal of Guidance, Control and Dynamics 16(1), 111–117.

Ogata, Katsuhiko (1993). Ingeniería de Control Moderna. 2a ed. Prentice Hall Hispanoamericana, S.A.

Saludes, Sergio and M. J. Fuente (2003). Fault tolerant fuzzy IMC control in a pH process. In: Proceedings of the European Control Conference. Cambridge (Reino Unido).

Saludes, Sergio and M. J. Fuente (2005). Support vector based novelty detection for fault tolerant control. In: Proceedings of the European Control Conference. Sevilla (España). pp. 5820–5825.

Sánchez, E. Gómez, J. M. Cano Izquierdo, M. J. Araúzo Bravo, Y. A. Dimitriadis and J. López Coronado (1998). Adaptive IMC using fuzzy neural networks for the control on non linear systems. In: The European Conference on Integration in Manufacturing. Goteborg, Suecia.

Wang, H. and Y. Wang (1999). Neural–network– based fault–tolerant control of unknown nonlinear systems. IEE Proceedings – Control Theory and Applications 146(5), 389–398.

Wang, H., M. Brown and C. J. Harris (1994). Fault detection for a class of unknown non– linear systems via associative memory networks. IMechE J, I, Syst. Control Eng. 79, 1415–1441.

Abstract Views

Metrics Loading ...

Metrics powered by PLOS ALM

Creative Commons License

Esta revista se publica bajo una Licencia Creative Commons Attribution-NonCommercial-CompartirIgual 4.0 International (CC BY-NC-SA 4.0)

Universitat Politècnica de València

e-ISSN: 1697-7920     ISSN: 1697-7912