Tuning nonlinear state-space models using unconstrained multiple shooting

Jan Decuyper, Mark C. Runacres, Johan Schoukens, Koen Tiels

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A persisting challenge in nonlinear dynamical modelling is parameter inference from data. Provided that an appropriate model structure was selected, the identication problem is profoundly affected by a choice of initialisation. A particular challenge that may arise is initialisation within a region of the parameter space where the model is not contractive. Exploring such regions is not feasible using the conventional optimisation tools for they require a bounded evaluation of the cost. This work proposes an unconstrained multiple shooting technique, able to mitigate stability issues during the optimisation of nonlinear state-space models. The technique is illustrated on simulation results of a Van der Pol oscillator and benchmark results on a Bouc-Wen hysteretic system.
Original languageEnglish
Title of host publicationTuning nonlinear state-space models using unconstrained multiple shooting
Number of pages7
Publication statusAccepted/In press - 27 Feb 2020


  • Unconstrained multiple shooting
  • Nonlinear state-space models
  • Nonlinear optimisation
  • Unstable initialisation

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