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Linear Parameter-Varying Two Rotor Aero-Dynamical System Modelling with State-Space Neural Network

  • Marcel LuzarEmail author
  • Józef Korbicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

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.

Keywords

Non-linear modelling Neural networks Linear parameter-varying system 

Notes

Acknowledgements

The work was supported by the National Science Centre of Poland under grant: UMO-2014/15/N/ST7/00749.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

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