Linear Bounds on an Uncertain Non-Linear Oscillator: An Info-Gap Approach

We study a 1-dimensional cubic non-linear oscillator in the frequency domain, in which the non-linearity is roughly estimated but highly uncertain. The task is to choose a suite of linear computational models at different excitation frequencies whose responses are useful approximations to, or upper bounds of, the real non-linear system. These model predictions must be robust to uncertainty in the non-linearity. A worst case for the uncertain non-linearity is not known. The central question in this paper is: how to choose the linear computational models when the magnitude of error of the estimated non-linearity is unknown. A resolution is proposed, based on the robustness function of info-gap decision theory. We also prove that the nonprobabilistic info-gap robustness is a proxy for the probability of success.