Load Identification in Bistable Spacecraft Booms via Parametric Data-Driven Modeling

TL;DR

This paper presents a parametric data-driven modeling approach using transfer functions to accurately estimate forces in bistable spacecraft booms from dynamic response data, improving onboard diagnostics.

Contribution

The authors develop a single parametric transfer-function model using p-AAA that captures nonlinear responses across load levels, reducing retesting needs and enhancing force estimation accuracy.

Findings

The parametric model reduced force estimation error by nearly 38%.

It outperformed discrete models in dynamic response prediction.

Validated with sinusoidal, triangular, and square excitation signals.

Abstract

Bistable tape spring booms are used on spacecraft for their ability to self-deploy using stored strain energy. However, their uncontrolled deployment can induce mechanical shocks that are variable as a function of material properties and temperature, and may damage sensitive satellite components and disrupt attitude control. Because traditional Finite Element Analysis (FEA) struggles to accurately capture this highly nonlinear behavior, we solve the inverse problem to estimate these loads from dynamic response measurements. Previous data-driven approaches using Vector Fitting required time-consuming retesting for every specific load level due to the boom's load-dependent dynamic behavior. To overcome this limitation, we introduce a parametric data-driven framework where a parametric transfer-function model of a composite tape spring boom is developed using force and velocity measurements. The parametric Adaptive Antoulas-Anderson algorithm (p-AAA) is used to construct a single parametric (multivariate) transfer function capable of capturing the nonlinear response of the boom to load amplitude. To evaluate the proposed framework, the boom is excited at its base at 15 distinct load levels using a single-axis reference input signal. Results demonstrate that the single parametric model outperformed the best discrete non-parametric case, reducing the total relative force estimation error for the reference signal by nearly 38\%. For experimental validation, the boom is subjected to sinusoidal, triangular and square signals. The cross validation results further supported this generalized performance. Collectively, these results show that the proposed parametric model accurately reconstructs input forces from velocity measurements alone, offering a solution for onboard diagnostics in future space missions.