Bayesian Filters for Parameter Identification of Duffing Oscillator

Vikas Kumar Mishra, Rahul Radhakrishnan, Abhinoy Kumar Singh, Shovan Bhaumik

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this paper, a joint state and parameter estimation problem of Duffing oscillator is explored using Bayesian filters, where the parameter to be identified is considered as an additional state variable. From a variety of Bayesian filters, the unscented Kalman filter (UKF), cubature Kalman filter (CKF) and Gauss-Hermite filter (GHF) are chosen for solving this problem. The performance of these filters are compared in terms of the root mean square error (RMSE) calculated over a specified number of Monte-Carlo runs. From simulation results, it is found that the accuracy of CKF and GHF are almost same while the computational time for GHF is almost three times higher.

Original languageEnglish
Pages (from-to)425-430
Number of pages6
Journal5th IFAC Conference on Advances in Control and Optimization of Dynamical Systems ACODS 2018
Issue number1
StatePublished - 1 Jan 2018
Externally publishedYes


  • Duffing oscillator
  • Higher order Gauss-Laguerre quadrature rule
  • multidimensional Gauss-Hermite quadrature rule
  • Nonlinear filtering
  • Third-degree spherical cubature rule

ASJC Scopus subject areas

  • Control and Systems Engineering


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