MIMO identification of frequency-domain unreliability in SEAs

We investigate the use of frequency domain identification and convex optimization for obtaining robust models of series elastic actuators. This early work focuses on identifying a lower bound on the ℋ
_∞
uncertainty, based on the non-linear behavior of the plant when identified under different conditions. An antagonistic testing apparatus allows the identification of the full two input, two output system. The aim of this work is to find a model which explains all the observed test results, despite physical non-linearity. The approach guarantees that a robust model includes all previously measured behaviors, and thus predicts the stability of never-before-tested controllers. We statistically validate the hypothesis that a single linear model cannot adequately explain the tightly clustered experimental results. And we also develop an optimization problem which finds a lower bound on the ℋ
_∞
uncertainty component of the robust models which we use to represent the plant in all the tested conditions.