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Abstrakty
In this paper, we present an expert network scheme designed to obtain discrete transfer functions for LTI systems under real sampling of finite duration rather than an instantaneous ideal one. For this purpose, the expert network handles two different identification methods to derive parametric discrete models techniques of reduced mathematical complexity from measured input-output data series. One of the methods is based on a typically used least-squares minimization, while the other one is based on the Leverrier's algorithm; that is, using a data series of the impulse response of the system to identify a parametric discrete model. These techniques are of particular practical interest when the continuous-time system is unknown or when dealing with discrete-time systems whose analytical expression becomes very complex due, for instance, to the use of finite duration real sampling. The expert network improves the discretization process implementing a biestimation mechanism that switches to the model that provides a better performance at each estimation instant considered for different values of the hold order.
Czasopismo
Rocznik
Tom
Strony
95--112
Opis fizyczny
Bibliogr. 32 poz.
Twórcy
autor
- Instituto Investigation Desarrollo de Procesos, (IIDP). Dpto. de Ingenieria de Sistemas Automatica, Fac. Ciencias de Leioa, 48080. Leioa, Spain
- Dpto. de Ingenieria de Sistemas y Automatica. EUITI de Bilbao, Pza. de la Casilla, 348012. Bilbao, Pza. de la Casilla, 3 4812. Bilbao, Universidad del Pais Vasco/University of the Basque Country.
autor
- Instituto Investigation Desarrollo de Procesos, (IIDP). Dpto. de Ingenieria de Sistemas Automatica, Fac. Ciencias de Leioa, 48080. Leioa, Spain
- Dpto. de Ingenieria de Sistemas y Automatica. EUITI de Bilbao, Pza. de la Casilla, 348012. Bilbao, Pza. de la Casilla, 3 4812. Bilbao, Universidad del Pais Vasco/University of the Basque Country.
autor
- Dpto. de Ingenieria de Sistemas y Automatica. EUITI de Bilbao, Pza. de la Casilla, 348012. Bilbao, Pza. de la Casilla, 3 4812. Bilbao, Universidad del Pais Vasco/University of the Basque Country.
autor
- Dpto. de Ingenieria de Sistemas y Automatica. EUITI de Bilbao, Pza. de la Casilla, 348012. Bilbao, Pza. de la Casilla, 3 4812. Bilbao, Universidad del Pais Vasco/University of the Basque Country.
Bibliografia
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- [2] Ljung L., System Identification, 2nd ed., Chapter 4, Prentice-Hall, Upper Saddle River, NJ, 1999.
- [3] Naslin P., Technologie et calcul pratique des systèmes asservis. Chapter 7, Dunod Eds., 1968.
- [4] Davies W. D. t., System Identification for Self-Adaptive Control, Chapter 2, Wiley-Interscience, 1970.
- [5] Ogata K., Discrete-Time Control Systems, Chapter 3, Prentice-Hall, 1994.
- [6]De la Sen M., Non periodic and adaptive sampling. A tutorial review. Informática Int. Journal, Vol. 7, No. 2, 1996,175-228.
- [7] Jury E. I., Sampled-Data Control Systems, Chapter 2, John Wiley & Sons Eds., New York 1958.
- [8] Akaike M., Identification of Transfer Function of Multipath Propagation in Frequency-Domain Delay-Distortion Equalization, lEICE Trans. Electron, Vol. E82-C, No. 7, 1999, 1267-1272.
- [9] Wan E. A., FIR neural networks for autoregressive time series prediction. Predicting the Future and Understanding the Past, Vol. XVII. Addison-Wesley, Reading, MA, 1993.
- [10] SSAS: Autonomous Systems and Assembly, Final Report MCR 84-1878, Denver, CO, Martin Marietta Aerospace, Nov. 1984.
- [11] Buchanan B. G., Shortliffe E. H., Completeness and Consistency in a Rule-Based System, Chapter 8, Addison-Wesley series in Artificial Intelligence, Rule-Based Expert Systems, Reading, MA: Addison-Wesley, 1984.
- [12] Scarl E. A., Jamiilson J., Delaune C., Knowledge-Based fault Monitoring and Diagnosis in Space Shuttle Propellant Loading, (Interim Report), Cambridge, MA, MITRE. 1984.
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- [15] De la Sen M., Minambres J. J., An expert system for optimal adaptive control automation. Fault Design and Reliability. Knowledge based and other approaches. Vol. 9, Pergamon Press, 1987, 75-83.
- [16] Soto J. C., Delà Sen M., Minambres J. J., Expert systems in monitoring chemical reactors, lasted Int. Symp. Applied Informatics, Grindelwald, 1988, 163-166.
- [17] Jackson P., Introduction to expert systems. International Computer Science Series, Addison-Wesley, Harlow, England, 1999.
- [18] Veloso M., Wooldridge M. J., Artificial Intelligence today: recent trends and developments. Lecture Notes on Artificial Intelligence 1600, Springer-Verlag, 1999.
- [19] Standard IEEE P1600.1, Standard Upper Ontology (SUO) Working Group, http://suo.ieee.org, 2003.
- [20] Devedzic V.. Understanding Ontological Engineering, Communications of the ACM, Vol. 45, No. 4, 2002, 136-144.
- [21] Devedzic V., Knowledge Modeling - State of the Art, Integrated Computer-Aided Engineering, Vol. 8, No. 3, 2001,257-281.
- [22] De la Sen M., Miñambres J. J., Garrido A. J., Logical Formal Description of Expert Systems, Informática Int. Journal, Vol. 13, No. 2, 2002, ISSN: 0868-4952, 2002, 177-208.
- [23] De la Sen M., Algebraic Properties and Design of Sampling Rates in Hybrid Linear Systems, Acta Applicandae Mathematicae, No. 72, 2002, 199-245.
- [24] De la Sen M., On the controller synthesis for linear hybrid systems, Asian Journal of Control, Vol. 1,No. 2, 1999,88-105.
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- [27] Garrido A. J., De la Sen M., Bârcena R., On the Validity of Realizable Physical Approximations to Sit) Input Impulses to obtain Impulse Responses. Application to synthesize Discrete-Time models for LTI Systems, Proceedings of the 5th WSES/IEEE CSCC 2001, 2001, 6631-6626.
- [28] Sévely Y., Systèmes et asservissements linéaires échantillonnés. Chapter 7, Dunod Eds., 1969.
- [29] Ljung L., System Identification Toolbox - For use with MATLAB, The Mathworks, Inc., 4th ed. 1995.
- [30] Phillips C. L., Nagle T. H., Digital Control System Analysis and Design using MATLAB, Prentice-Hall, 1994.
- [31] Aràndiga F., Donat R., Harten A., Multiresolution Based on Weighted Averages of the Hat Function II: Nonlinear Reconstruction Techniques, SIAM Journal on Scientific Computing, Vol. 20, No. 3, 1999, 1053-1093.
- [32] Helmke U., Shayman M. A., Critical Points of Matrix Least-Squares Distance Functions, Technical Reports of The Institute of Systems Research, N. 92-30 University of Maryland, 1992.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPW4-0002-0132