Non-Filippov dynamics arising from the smoothing of nonsmooth systems, and its robustness to noise

Mike R Jeffrey, David J. W. Simpson

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)
269 Downloads (Pure)


Switch-like behaviour in dynamical systems may be modelled by highly nonlinear functions, such as Hill functions or sigmoid functions, or alternatively by piecewise-smooth functions, such as step functions. Consistent modelling requires that piecewise-smooth and smooth dynamical systems have similar dynamics, but the conditions for such similarity are not well understood. Here we show that by smoothing out a piecewise-smooth system one may obtain dynamics that is inconsistent with the accepted wisdom — so-called Filippov dynamics — at a dis- continuity, even in the piecewise-smooth limit. By subjecting the system to white noise, we show that these discrepancies can be understood in terms of potential wells that allow solutions to dwell at the discontinuity for long times. Moreover we show that spurious dynamics will revert to Filippov dynamics, with a small degree of stochasticity, when the noise magnitude is sufficiently large compared to the order of smoothing. We apply the results to a model of a dry-friction os- cillator, where spurious dynamics (inconsistent with Filippov’s convention or with Coulomb’s model of friction) can account for different coefficients of static and ki- netic friction, but under sufficient noise the system reverts to dynamics consistent with Filippov’s convention (and with Coulomb-like friction).
Original languageEnglish
Pages (from-to)1395-1410
Number of pages16
JournalNonlinear Dynamics
Issue number2
Early online date12 Jan 2014
Publication statusPublished - 1 Apr 2014


  • Dynamics
  • Friction
  • Switching
  • White noise
  • Regularization
  • Fokker-Planck
  • Discontinuity
  • Nonsmooth

Fingerprint Dive into the research topics of 'Non-Filippov dynamics arising from the smoothing of nonsmooth systems, and its robustness to noise'. Together they form a unique fingerprint.

Cite this