论文标题
使用基于控制的延续，在过程噪声存在下非线性参数识别的鲁棒性
Robustness of nonlinear parameter identification in presence of process noise using control-based continuation
论文作者
论文摘要
在这项研究中，我们考虑了在过程噪声存在下非线性结构的实验性，周期性的反应。基于控制的延续用于测量稳定和不稳定的周期性解决方案，而不同级别的噪声则被注入系统。使用这些数据，通过识别类似于Duffing的模型的参数来评估基于控制的延续算法的鲁棒性及其捕获无噪声系统响应的能力。我们证明，基于控制的延续提取物在存在高噪声的情况下比开环参数扫描更强大，因此是研究非线性结构的宝贵工具。
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.
