Abstract
In every model-based approaches, i.e., fault diagnosis, fuzzy control, robust fault-tolerant control, the exact model is crucial. This paper presents a methodology which allows to obtain an exact model of high-order, non-linear cross-coupled system, namely Two Rotor Aero-dynamical System (TRAS), using a state-space neural network. Moreover, the resulting model is presented in a linear parameter-varying (LPV) form making it easier to analyze (i.e., its stability and controllability) and control. Such a form is obtained by direct transformation of the neural network structure into quasi-LPV model. For the neural network modelling, a SSNN Toolbox is utilized.
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Acknowledgements
The work was supported by the National Science Centre of Poland under grant: UMO-2014/15/N/ST7/00749.
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Luzar, M., Korbicz, J. (2018). Linear Parameter-Varying Two Rotor Aero-Dynamical System Modelling with State-Space Neural Network. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_52
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DOI: https://doi.org/10.1007/978-3-319-91262-2_52
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