Abstract
In this chapter several nonlinear model representations are presented along with a general methodology for nonlinear system modelling. Polynomial NARMAX and neural network models are presented in more detail and nonlinear models for the engine are estimated. It is clear that in order to model the global dynamics of the gas turbine a nonlinear model is required.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schetzen M. The Volterra and Wiener Theories of Nonlinear Systems. New York: Wiley-Interscience, 1980.
Leontaritis I J, Billings SA. Experimental design and identiflability for nonlinear systems. IntJ. Systems Science, 1987;18:189–202.
Narendra KS, Gallman PG. An iterative method for the identification of nonlinear systems using a Hammerstein model. IEEE Trans. Automatic Control, 1966;11:546–550.
Billings SA, Tsang KM. Spectral analysis of block-structured nonlinear systems. J. Mechanical Systems and Signal Processing, 1990;4:117–130.
Weiss M, Evans C, Rees D. Identification of nonlinear cascade systems using paired multisine signals. IEEE Trans, on Instrumentation and Measurement, 1998;47:332–336.
Vandersteen G, Schoukens J. Measurement and identification of nonlinear systems consisting of linear dynamic blocks and one static nonlinearity. IEEE Trans. Automatic Control, 1999;44:1266–1271.
Leontaritis IJ, Billings SA. Input-output parametric models for nonlinear systems. Part I: deterministic nonlinear systems. Int. J. Control, 1985;41:303–328.
Billings SA, Voon WSF Piecewise identification of nonlinear systems. Int. J. of Control, 1987;46:215–235.
Johansen TA, Foss BA Constructing NARMAX models using ARMAX models. Int. J. Control, 1993;58:1125–1153.
Sjöberg J Zhang Q, Ljung L, Benveniste A, Delyon B, Glorennec P, Hjalmarsson H, Juditsky A. Nonlinear black-box modeling in system identification: A unified overview. Automatica, 1995;31:1691–1724.
Ljung L. Black-box models from input-output measurements. In: Proc. IEEE Instrumentation and Measurement Technology Conf., Budapest, 2001; 138–146.
Ljung L. System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1987.
Chen S, Billings SA, Luo W. Orthogonal least squares methods and their application to nonlinear system identification. Int. J. of Control, 1989; 50:1873–1896.
Korenberg MJ, Billings SA, Liu YP, Mollroy PJ. Orthogonal parameter estimation algorithm for nonlinear stochastic systems. Int. J. of Control, 1988; 48:193–210.
Aguirre LA. Some remarks on structure selection for nonlinear models. Int. J. Bifurcation and Chaos, 1994;4:1707–1714.
Liu YP. Identification of nonlinear systems: The NARMAX polynomial model approach. Ph.D. dissertation. University of Sheffield, Department of Automatic Control and Systems Engineering, UK, 1988.
Billings SA, Voon WSF. Structure detection and model validity tests in the identification of nonlinear systems. IEE Proceedings, Pt.D. 1983; 130:193–199.
Aguirre LA, Donoso-Garcia PF, Santos-Filho R. Use of a priori knowledge in the identification of global nonlinear models: A case study using a buck converter. IEEE Trans. Circuits and Systems, 2000;47:1081–1085.
Nørgaard M, Ravn O, Poulsen KN, Hansen LK. Neural Networks for Modelling and Control. London: Springer-Verlag, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag London
About this chapter
Cite this chapter
Kulikov, G.G., Thompson, H.A. (2004). Nonlinear Gas Turbine Modelling. In: Kulikov, G.G., Thompson, H.A. (eds) Dynamic Modelling of Gas Turbines. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-3796-2_8
Download citation
DOI: https://doi.org/10.1007/978-1-4471-3796-2_8
Publisher Name: Springer, London
Print ISBN: 978-1-84996-914-7
Online ISBN: 978-1-4471-3796-2
eBook Packages: Springer Book Archive