Control por Planificación de Ganancia con Modelos Borrosos
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https://doi.org/10.4995/riai.2004.8022Palabras clave:
control con modelos locales, planificación de ganancia, técnicas de control inteligente, sistemas borrososResumen
En este artículo se presentan los tipos de modelos borrosos y metodologías de identificación (por agrupamiento borroso) más adecuados para obtener modelos locales de sistemas no lineales. En particular, se muestra qué técnicas de control por planificación de ganancia son aplicables a los modelos así identificados. Estas técnicas, basándose en el diseño de controladores lineales para los modelos locales identificados, consiguen obtener de forma sencilla controladores para un sistema borroso.Descargas
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Aström, K. J. and B. Wittenmark (1995). Adaptive Control. Ed. Addison-Wesley. Reading, Massachusetts, USA.
Babuska, R. (1998). Fuzzy Modeling for Control. Ed. Kluwer Academic. Boston, USA.
Bezdek,J.C.(1987). Pattern recognition with Fuzzy Objective Function Algorithms. Ed. Plenum Press. NewYork, USA.
Boyd, S., L. El Ghaoui, E. Feronand V. Balakrishnan (1994). Linear matrix inequalities in system and control theory. Ed. SIAM. Philadelphia, USA.
Desoer, C. A. (1969). Slowly varying controller dx/dt=a(t)x. IEEE Transactions on Automatic Control 14, 780–781.
Díez, J. L. (2003). Técnicas de agrupamiento para identificación y control por modelos locales. Tesis Doctoral, DISA. Universidad Politecnica de Valencia.
Díez, J. L., A. Sala and J. L. Navarro (2002). Fuzzy clustering algorithm for local model control. Proc. IFAC 15th World Congress pp. 60–66.
Díez, J. L. and F. Previdi (2001). Guaranteed closed Loop precision in multiple model based control. Proc. IFAC Workshop on Adaptation and Learning in Control and Signal Processing pp. 139–144.
Díez, J. L., J. L. Navarro and A. Sala (2004). Algoritmos de agrupamiento en la identificación demodelos borrosos. RIAI: Revista Iberoamericana de Automática e Informática Industrial (enviado).
Guerra, T-M. and L. Vermeiren (2001). Conditions for non quadratic stabilization of discrete fuzzy models. Proc. IFAC Workshop on Advanced Fuzzy-Neural Control pp. 1–6.
Hunt, K.J. and T.A. Johansen (1997). Design and analysis of gain-scheduled control using local controller networks. International Journal of Control 66, 619–651.
Hunt, K. J. and T. A. Johansen (1997). Design and analysis of gain-scheduled control using local controller networks. International Journal of Control 66, 619–651.
Hunt, K. J., R. Haasand R. Murray-Smith (1996). Extending the funcional equivalence of radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks 7, 776–781.
Johansen, T.A. and R. Murray-Smith (1997). The operating regime approach to nonlinear modelling and control. In: Multiple model approaches to modelling and control (R. Murray-Smith and T.A. Johansen, Eds.). Ed. Taylor & Francis. London, UK.
Johansson, R. (1993). System modeling and identification. Ed. Prentice-Hall, Information and System Sciences series. New Jersey, USA.
Leith, D. J. and W. E. Leithead (2000). Survey of gain scheduling analysis and design. International Journal of Control 73, 1001–1025.
Leith, D.J. and W.E. Leithead (1998). Gain-scheduledand nonlinear systems: dynamic analysis by velocity-based linearisation families. International Journal of Control 70, 289–317.
Leith, D.J. and W.E. Leithead (1999). Analytic framework for blended multiple model systems using linear local models. International Journal of Control 72, 605–619.
Rugh, W. J. and J. S. Shamma (2000). Research on gain scheduling. Automatica 36, 1401–1425.
Sandberg, I. W. (1964). In the l2-boundedness of solutions of nonlinear functional equations. Bell Sys. Tech. J. 43, 1581–15997.
Schulte, H. and H. Hahn (2004). Fuzzy state feedback gain scheduling control of servopneumatic actuators. Control Engineering Practice 12 (5), 639–650.
Stein, G., G.L. Hartmann and R. Hendrick (1977). Adaptive control laws for f-8 flight test. IEEE Transactions on Automatic Control 22(5), 758–767.
Takagi, T. and M. Sugeno (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on System, Man and Cybernetics 15, 116–132.
Tanaka, K. and H. O. Wang (2001). Fuzzy control systems design and analysis. Ed. John Wiley & Sons. New York, USA.
Tanaka, K. and M. Sugeno (1992). Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems 45, 135–156.
Walter, E. and L. Pronzato (1997). Identification of Parametric Models from Experimental Data. Ed. Springer-Verlag. London, UK.
Wang, L.-X. (1997). A Course in Fuzzy Systems and Control. Ed. Prentice-Hall. New Jersey, USA.
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