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Robustness of nonlinear parameter identification in presence of process noise using control-based continuation

In this study, we consider the experimentally-obtained, periodically-forced response of a nonlinear structure in the presence of process noise. Control-based continuation is used to measure both the stable and unstable periodic solutions while different levels of noise are injected into the system. Using this data, the robustness of the control-based continuation algorithm and its ability to capture the noise-free system response is assessed by identifying the parameters of an associated Duffing-like model. We demonstrate that control-based continuation extracts system information more robustly, in the presence of a high level of noise, than open-loop parameter sweeps and so is a valuable tool for investigating nonlinear structures.