Abstract
The excitation of a system is a crucial task in system identification. If the data was not captured with persistent excitation, the parameter estimation degrades and the parameter variance increases. Application of Design of Experiment (DoE) algorithms helps to overcome these problems. This work presents the extension of the well known Wynn-Algorithm for non-linear but static systems to non-linear dynamic systems. Instead of optimizing the input only for the next sampling instant, an input sequence is optimized over a receding horizon.
The developed algorithm is used to identify the model of a hydrodynamic brake used on a combustion engine test bench as load machine.
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References
Billings, S.A., Chen, S., Korenber, M.J.: Identification of mimo non-linear systems using a forward-regression orthogonal estimator. International Journal of Control 49, 2157–2189 (1989)
Hjalmarsson, H., Martensson, J.: Optimal input design for identification of nonlinear systems: Learning from the linear case. In: 2007 American Control Conference, New York, US (2007)
Hodgson, P.G., Raine, J.K.: Computer simulation of a variable fill hydraulic dynamometer. Part 2: steady state and dynamic open-loop performance. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 206(13), 49–56 (1992)
Hodgson, P.G., Raine, J.K.: Computer simulation of a variable fill hydraulic dynamometer. Part 3: closed-loop performance. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 206(53), 327–336 (1992)
Kozek, M., Sinanovic, S.: Identification of wiener models using optimal local linear models. Simulation Modelling Practice and Theory 16(8), 1055–1066 (2008)
Larsson, C., Hjalmarsson, H., Rojas, C.R.: On optimal input design for nonlinear systems. In: 49th IEEE Conference on Decision and Control, Atlanta, Georgia, USA (2010)
Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs (1999)
Manchester, I.R.: An algorithm for amplitude-constrained input design for system identification. In: 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P. R. China (2010)
Nelles, O.: Nonlinear System Identification. Springer, Heidelberg (2001)
Piroddi, L., Spinelli, W.: An identification algorithm for polynomial narx models based on simulation error minization. International Journal of Control 76, 1767–1781 (2003)
Pronzato, L.: Optimal experimental design and some related control problems. Automatica 44, 303–325 (2008)
Pukelsheim, F.: Optimal Design of Experiment, 2nd edn. John Wiley & Sons, Chichester (2006)
Raine, J.K., Hodgson, P.G.: Computer simulation of a variable fill hydraulic dynamometer. Part 1: torque absorption theory and the influence of working compartment geometry on performance. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 205(33), 155–163 (1991)
Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.-Y., Hjalmarsson, H., Juditsky, A.: Nonlinear black-box modeling in system idenfication: a unified overview. Automatica 31, 1691–1724 (1995)
Vetr, M., Passenbrunner, T.E., Trogmann, H., Ortner, P., Kokal, H., Schmidt, M., Paulweber, M.: Control oriented modeling of a water brake dynamometer. In: 2010 IEEE Multi-Conference on Systems and Control, Yokohama, Japan, September 8-10 (2010)
Wynn, H.P.: The sequential generation of d-optimum experimental designs. The Annals of Mathematical Statistics 41, 1655–1664 (1970)
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Hirsch, M., Passenbrunner, T.E. (2012). Extension of Static Non-linear DoE Identification Algorithms to Dynamic Systems. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27579-1_5
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DOI: https://doi.org/10.1007/978-3-642-27579-1_5
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