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Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 19, 2013 - Issue 6
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Original Articles

Incremental optimal process excitation for online system identification based on evolving local model networks

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Pages 505-525 | Received 14 Aug 2012, Accepted 23 Apr 2013, Published online: 22 May 2013

References

  • L. Ljung, System Identification: Theory for the User, 2nd ed., Prentice Hall Inc., Upper Saddle River, NJ, 1999.
  • L. Pronzato, Optimal experimental design and some related control problems, Automatica. 44 (2008), pp. 303–325.
  • G.C. Goodwin and R.L. Payne, Dynamic System Identification: Experiment Design and Data Analysis, Academic Press Inc., New York, 1977.
  • M. Deflorian, F. Kloepper, and J. Rueckert, Online dynamic black box modelling and adaptive experiment design in combustion engine calibration, 6th IFAC Symposium Advances in Automotive Control 2010, Holiday Inn Munich, IFAC, Laxenburg, 12–14 July 2010, pp. 1–6.
  • C. Hametner and S. Jakubek, Combustion engine modelling using an evolving local model network, Proceedings of the 2011 International Conference on Fuzzy Systems (FUZZ IEEE 2011), Taipei, 27–30 June 2011.
  • S. Jakubek and C. Hametner, Identification of neurofuzzy models using GTLS parameter estimation, IEEE Trans. Syst. Man Cyber. B. 39 (2009), pp. 1121–1133.
  • E. Lughofer, FLEXFIS: A robust incremental learning approach for evolving Takagi-Sugeno Fuzzy models, IEEE Trans. Fuzzy Syst. 16 (2008), pp. 1393–1410.
  • C. Hametner and S. Jakubek, Engine model identification using local model networks in comparison with a multilayer perceptron network, Proceedings of the 2nd International Multi-Conference on Complexity, Informatics and Cybernetics: IMCIC 2011, Orlando, FL, 27–30 March 2011.
  • O. Nelles, Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, Springer, Berlin, 2001.
  • E. Camacho and C. Bordons, Model Predictive Control, Springer, London, 2000.
  • E. Walter and L. Pronzato, Identification of Parametric Models from Experimental Data, Springer, Berlin, 1997.
  • G. Franceschini and S. Macchietto, Model-based design of experiments for parameter precision: State of the art, Chem. Eng. Sci. 63 (2008), pp. 4846–4872.
  • T. Santner, B.J. Williams, and W. Notz, The Design and Analysis of Computer Experiments (Springer Series in Statistics), Springer, New York, 2003.
  • S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, New York, 2004.
  • D. Luenberger and Y. Ye, Linear and Nonlinear Programming, Springer, New York, 2008.
  • S. Richter, C. Jones, and M. Morari, Real-time input-constrained MPC using fast gradient methods, Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC2009. Proceedings of the 48th IEEE Conference on, Shanghai, 15–18 December 2009.
  • H.J. Ferreau, H.G. Bock, and M. Diehl, An online active set strategy to overcome the limitations of explicit MPC, Int. J. Robust Nonlinear Control. 18 (2008), pp. 816–830.
  • Y. Wang and S. Boyd, Fast model predictive control using online optimization, IEEE Trans. Control Syst. Technol., 18 (2010), pp. 267–278.
  • E.A. Yildirim and S.J. Wright, Warm-Start strategies in interior-point methods for linear programming, SIAM J. Optimiz. 12 (2002), pp. 782–810.
  • C. Hametner, M. Stadlbauer, M. Deregnaucourt, S. Jakubek, and T. Winsel, Optimal experiment design based on local model networks and multilayer perceptron networks, Eng. Appl. Artif. Intel. 26 (2012), pp. 251–261.
  • J.C. Wurzenberger, R. Heinzle, A. Schuemie, and T. Katrasnik, Crank-Angle resolved real-time engine simulation – integrated simulation tool chain from office to testbed, SAE 2009 World Congress (2009) Number 2009-01-0589 in Technical Paper Series, SAE International, Warrendale, PA.
  • J.C. Wurzenberger, P. Bartsch, and T. Katrasnik, Crank-Angle resolved real-time capable engine and vehicle simulation – fuel consumption and driving performance, SAE 2010 World Congress (2010) Number 2010-01-0784 in Technical Paper Series, SAE International, Warrendale, PA.
  • M. Stadlbauer, M. Deregnaucourt, C. Hametner, S. Jakubek, and T. Winsel, Online measuring method using an evolving model based test design for optimal process stimulation and modelling, Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, Graz, IEEE, New York, 13–16 May 2012, pp. 1314–1319.
  • R. Van Der Merwe, Sigma-point Kalman filters for probabilistic inference in dynamic state-space models, University of Stellenbosch, Stellenbosch, 2004.
  • S.M. Baker, C.H. Poskar, and B.H. Junker, Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models, EURASIP J. Bioinforma. Syst. Biol. 2011 (2011), pp. 1–8.
  • R. Schenkendorf, A. Kremling, and M. Mangold, Optimal experimental design with the sigma point method, IET Syst. Biol. 3 (2009), pp. 10–23.
  • T. Heine, M. Kawohl, and R. King, Derivative-free optimal experimental design, Chem. Eng. Sci. 63 (2008), pp. 4873–4880.

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