Onboard estimation of unknown dynamics of flexible spacecraft - Woodward, Nicolo; Bevilacqua, Riccardo (Acta astronautica, 2024)
Attitude control methods of highly flexible spacecraft have seen increased interest over the last decades thanks to the technological development of flexible solar panels and deployables, which improves the capabilities of satellites. However, a high-fidelity model of the flexible dynamics is hard to obtain on-ground testing because not all modes can be observed, complicating the controller design. This paper proposes a method to develop a high-fidelity model of a spacecraft with a flexible appendage subject to large deformations by modeling it as a finite series of rigid links connected by torsional springs and dampers. To overcome the uncertainties or unknowns in the flexible dynamics, an onboard estimation through an adaptive controller is performed for these unknowns while the spacecraft is maneuvered. The controller uses integral concurrent learning (ICL), an adaptive scheme that records inputs and outputs provided by sensors mounted on the flexible body. The novelty of this investigation is the development of self-adapting control gains for the learning matrix obtained from ICL. This approach achieves the objective of tracking a desired trajectory while accurately learning the unknown physical parameters of the flexible appendage by only using the recorded measurements of the appendage displacements and the spacecraft attitude and velocities. It was observed that for a finer discretization of the flexible appendage and therefore a higher fidelity model of the flexible dynamics, the estimation algorithm is able to observe all the frequencies necessaries to learn the unknown mechanical properties of the flexible body. •Modeling a flexible deployable as finite chain of rigid links connected by unknown torsional joints to consider large deformations and reduce onboard computational effort.•Accurately estimated unknown flexible dynamics using integral concurrent learning (ICL).•Introduced a self-tuning algorithm for eigenvalue placement of the learning matrix to guarantee the convergence of any number of estimated unknowns to their true value.