Abstract
In this paper a comparative study of three nonlinear model order reduction techniques using a case study of a transmission line model is presented. The investigated model order reduction techniques are: quadratic approximation, trajectory piecewise linear approximation and data-based identification of bilinear model. The performance of model order reduction techniques has been evaluated in terms of their accuracy and computational cost. The original 100th order nonlinear model is reduced to 12th and 20th order models by using three different MOR techniques yet preserving simulation accuracy.
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Acknowledgements
This work is part of the EPSRC funded FUTURE Vehicles project (EP/I038586/1). The authors also acknowledge the support from project partners.
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Aizad, T., Maganga, O., Sumislawska, M., Burnham, K.J. (2015). A comparative Study of Model-Based and Data-Based Model Order Reduction Techniques for Nonlinear Systems. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_12
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