A Data-Driven Surrogate Modeling and Sensor/Actuator Placement Framework for Flexible Spacecraft

TL;DR

This paper introduces a data-driven framework using Dynamic Mode Decomposition for creating reduced-order models of flexible spacecraft, optimizing sensor and actuator placement for improved control and estimation.

Contribution

It presents a novel data-driven methodology for surrogate modeling and sensor/actuator placement that does not rely on explicit analytical models, suitable for onboard control systems.

Findings

Achieves substantial model-order reduction while maintaining dynamic fidelity.

Provides an effective approach for optimal sensor-actuator configuration.

Demonstrates applicability to nonlinear flexible spacecraft models.

Abstract

Flexible spacecraft structures present significant challenges for physical and control system design due to nonlinear dynamics, mission constraints, environmental variables, and changing operational conditions. This paper presents a data-driven framework for constructing reduced-order surrogate models of a flexible spacecraft using the method of Dynamic Mode Decomposition (DMD), followed by optimal sensor/actuator pair placement. Highfidelity simulation data from a nonlinear flexible spacecraft model, including coupled rigid-body and elastic modes, are captured by defining a mesh of nodes over the spacecraft body. The data-driven methods are then used to construct a modal model from the time histories of these node points. Optimal sensor/actuator placement for controllability and observability is performed via a nonlinear programming technique that maximizes the singular values of the Hankel matrix. Finally, the sensor placement and dynamics modeling approach is iterated to account for changes in the dynamic system introduced by sensor/actuator physical mass. The proposed methodology enables initialization of physical modeling without requiring a direct analytical model and provides a practical solution for onboard implementation in model-based control and estimation systems. Results demonstrate optimal design methodology with substantial model-order reduction while preserving dynamic fidelity, and provide insight into effective sensor-actuator configurations for estimation and control.