Hybrid Control and Estimation for Spacecraft Close Proximity Missions
Author
Malladi, Bharani PrabhaIssue Date
2019Keywords
Close Proximity MissionsDualquaternions
Hybrid Kalman filter
Hybrid systems
Spacecraft
Supervisory control
Advisor
Butcher, Eric A.Sanfelice, Ricardo G.
Metadata
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The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Embargo
Release after 08/27/2020Abstract
Recent developments in the field of automation and control have motivated the use of new approaches and strategies for control and navigation in advanced space missions. These space missions to name a few include spacecraft rendezvous, proximity operations, docking, space situational awareness, orbital debris removal, and rendezvous to non-cooperative free-flying space objects. Such advanced space missions present several challenges and may require switching between multiple strategies to achieve robust performance. In most practical applications, the data required to design such advanced switching strategies may be affected by environmental perturbations. Hence, the work in this thesis focuses on the study of robust control and estimation techniques applied to spacecraft close-proximity missions. Mainly, control and estimation strategies necessary for these missions are studied in the hybrid systems approach with the necessary mathematical rigor both in terms of theory and numerical examples. Hybrid systems are dynamical systems with both continuous and discrete dynamics. Details regarding this hybrid formulation is introduced and the advantages of such formulation in achieving robust performance is discussed. Two main ideas are presented in this dissertation: 1) hybrid control and estimation strategies are implemented for a point mass spacecraft relative motion model; 2) a hybrid control strategy is extended to rigid body translational and rotational dynamics in the framework of dual quaternions with robust performance. First, a hybrid supervisory control strategy is implemented for a spacecraft close- proximity mission that includes rendezvous, docking, and relocation maneuver. Considering that range and angle measurements are available, a Kalman filter approach in the presence of intermittent measurements is implemented to estimate the position and velocity of the spacecraft. With this estimated position and velocity, feed- back controllers are designed to control the spacecraft during each of the rendezvous, docking and relocation maneuvers. Next, a hybrid supervisory strategy is formulated to robustly switch between the previously designed controllers based on mission requirements. This method includes designing a hysteresis(overlap)-based algorithm to effciently switch between various controllers in the presence of system and environmental disturbances. These control and estimation strategies are implemented on a point mass spacecraft relative motion model. The second part of this work is motivated by certain space missions that, based on structural limitations and environmental disturbances, might require coupled orbital- attitude control. The dual quaternion framework is formulated to model such coupled orbital-attitude dynamics called dual quaternions is introduced for a generic rigid body. A hysteresis-based switching strategy is implemented to control the rigid body to the desired position and orientation with and without angular velocity measure- ments. Specifically, control algorithms that provide robust performance to combined translational and rotational motion in the presence of disturbances are presented. Next, this control strategy is extended to spacecraft docking maneuver and the robustness of such an algorithm in the presence of disturbances is discussed in detail.Type
textElectronic Dissertation
Degree Name
Ph.D.Degree Level
doctoralDegree Program
Graduate CollegeMechanical Engineering