System Identification near a Hopf Bifurcation via the Noise-Induced Dynamics in the Fixed-Point Regime

TL;DR

This paper introduces a novel system identification framework that uses noise-induced dynamics in the pre-bifurcation regime to predict Hopf bifurcation points and post-bifurcation behavior from experimental data.

Contribution

It presents a new approach capable of predicting bifurcation points and dynamics solely from pre-bifurcation data, applicable to various experimental systems.

Findings

Successfully identified bifurcation nature in three systems

Predicted bifurcation points and limit-cycle features

Demonstrated effectiveness with only pre-bifurcation data

Abstract

A Hopf bifurcation is prevalent in many nonlinear dynamical systems. When a system prior to a Hopf bifurcation is exposed to a sufficient level of noise, its noise-induced dynamics can provide valuable information about the impending bifurcation. In this thesis, we present a system identification (SI) framework that exploits the noise-induced dynamics prior to a Hopf bifurcation. The framework is novel in that it is capable of predicting the bifurcation point and the post-bifurcation dynamics using only pre-bifurcation data. Specifically, we present two different versions of the framework: input-output and output-only. For the input-output version, the system is forced with additive noise generated by an external actuator, and its response is measured. For the output-only version, the intrinsic noise of the system acts as the noise source and only the output signal is measured. In both versions, the Fokker-Planck equations, which describe the probability density function of the fluctuation amplitude, are derived from self-excited oscillator models. Then, the coefficients of these models are extracted from the experimental probability density functions characterizing the noise-induced response in the fixed-point regime. The SI framework is tested on three different experimental systems: a low-density jet, a flame-driven Rijke tube, and a gas-turbine combustor. For these systems, we demonstrate that the proposed framework can identify the nature of the Hopf bifurcation and the system's order of nonlinearity. Moreover, by extrapolating the identified model coefficients, we are able to forecast the locations of the bifurcation points and the limit-cycle features after those points. To the best of our knowledge, this is the first time that SI has been performed using data from only the pre-bifurcation regime, without the need for knowledge of the location of the bifurcation point.