Speaker
Description
Bistable tape spring booms are used in space structures for their ability to self-deploy using stored strain energy. However, their uncontrolled deployment can induce mechanical shocks that may damage sensitive satellite components. In the past, researchers have focused on mitigating these shocks through damping materials and active control techniques. Accurate prediction of deployment-induced loads, however, remains a challenge. This work presents a data-driven framework for solving the inverse problem for estimating these loads from dynamic response measurements. A tape spring boom is excited at its base at 15 distinct load levels using a single-axis reference signal to assess the impact of varying load levels and the velocity at its tip is measured using a laser vibrometer. As a first step, Vector Fitting is used to create reduced order transfer functions for each load case. For generalization, the parametric Adaptive Anderson Antoulas (p-AAA) algorithm, is then used to generate a unified state-space model capable of accurately capturing the system's load dependent behavior. To avoid data redundancy, the Discrete Empirical Interpolation Method (DEIM) is implemented to systematically rank and select the minimal subset of load levels required to capture the dominant system dynamics. For experimental validation, the boom is subjected to various test signals and input forces are estimated from the measured responses and compared to the known input excitations. Finally, the entire process is validated: the force inversely calculated from an actual deployment test’s tip velocity is applied back to the boom via the shaker, and the resulting tip velocity is measured and compared to the original deployment velocity, thereby enabling non-intrusive dynamic load characterization. This process enables a systematic evaluation of the estimation technique’s accuracy for real deployment scenarios.