Online system identification in a Duffing oscillator by free energy minimisation

TL;DR

This paper introduces a novel online system identification method for a Duffing oscillator using free energy minimisation, enabling real-time parameter estimation with performance comparable to offline methods.

Contribution

The paper presents a new variational message passing approach for online identification of nonlinear stochastic systems, demonstrated on a Duffing oscillator.

Findings

Performs comparably to offline prediction error minimisation

Validated on electronic Duffing oscillator data

Effective for real-time nonlinear system identification

Abstract

Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals. Here, the nonlinear stochastic differential equation of a Duffing oscillator is cast to a generative model and dynamical parameters are inferred using variational message passing on a factor graph of the model. The approach is validated with an experiment on data from an electronic implementation of a Duffing oscillator. The proposed inference procedure performs as well as offline prediction error minimisation in a state-of-the-art nonlinear model.