Date of Award

Spring 5-8-2023

Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Aerospace Engineering

Committee Chair

Riccardo Bevilacqua

First Committee Member

Morad Nazari

Second Committee Member

Eduardo Rojas

College Dean

James Gregory


Attitude controls methods of highly flexible spacecraft have seen increased interest over the last decades thanks to the technological development of flexible solar panels and deploy-ables, which improves the capabilities of small satellites. However, a high-fidelity model of the flexible mode dynamics is hard to obtain in on-ground testing because not all modes of frequencies can be observed, complicating the controller design. Furthermore, plastic deformations due to long periods of storage of stowed flexible components could result in exciting frequencies outside of the designed controller’s bandwidth, leading to an uncontrollable system. This thesis 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 in the flexible dynamics, an onboard estimation through an adaptive controller is performed for these un- knowns 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 both the tracking error and the learning matrix obtained from ICL. After tuning the controller for the system’s initial conditions, it 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. 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.