Dissertation/Thesis Abstract

The Location-Scheduled Control Methodology as Applied to Nanosatellites
by Sorgenfrei, Matthew Charles, Ph.D., University of California, Davis, 2013, 101; 3565559
Abstract (Summary)

The problem of controlling the behavior of a spacecraft in an optimal manner is one that has been studied since the beginning of the space era in the late 1950s. Recently, the complexity of such optimization problems has been increased by the introduction of spacecraft that are comparatively small in size and capable of being reconfigured, either in between missions or on-orbit. While such spacecraft have the potential to greatly expand the variety of missions that can be undertaken, they also increase the basic number of design variables that must be optimized. This dissertation presents a novel approach for the design of spacecraft control systems that optimizes both controller gain parameters and physical attributes of the spacecraft in parallel. The central design tool for this new strategy is a genetic algorithm, which applies concepts from evolutionary biology to search a complex design space in an efficient manner. Results are presented for spacecraft of various sizes, and the genetic algorithm design results are compared to a number of more traditional design approaches.

In the first part of this dissertation the genetic algorithm is applied to the problem of tuning the gain parameters of a nonlinear control law. This controller is used within a small spacecraft performing an attitude tracking maneuver, and must compensate for multiple environmental disturbance moments and the imposition of actuator saturation limits. While the control law under study has proven stability properties, no work has yet been done on optimizing the gain parameters for a specific application. The combined complexity of the controller itself and the spacecraft system make gain tuning via traditional approaches very difficult, and as such a genetic algorithm is utilized. The genetic algorithm can search a broad swath of the overall design space, and does so more efficiently than a human engineer applying their intuition in an "informed" trial-and-error approach to the same problem.

With the basic efficacy of the genetic algorithm established, in the next phase of the dissertation a novel controller optimization approach known as location-scheduled control is introduced. Under location-scheduled control, the use of the genetic algorithm is extended to not only optimize the gain parameters of a given control law but also the physical location of the control actuators within the spacecraft. This dual optimization of both controller gain parameters and physical properties of the spacecraft yields a catalog of design solutions that can be called upon from mission to mission, which significantly reduces the time required for control system design. The ability to easily relocate the hardware components of a spacecraft control system is enabled by a class of spacecraft known as CubeSats, which will be described in detail.

In the final portion of this dissertation the location-scheduled control methodology is applied to a real-word testbed system and hardware results are compared to those obtained via simulation. This system makes use of a unique property of superconducting physics known as flux-pinned interfaces that allows CubeSat-scale test articles to be easily reconfigured. The location-scheduled control approach is used to simultaneously determine the optimal configuration of the reconfigurable spacecraft system and the appropriate gains for a single-axis reorientation maneuver. It is shown through hardware testing that the genetic algorithm once again yields a combination of system configuration and controller gain values that outperforms those found by a control systems engineer.

Indexing (document details)
Advisor: Joshi, Sanjay S.
Commitee: Gundes, Nasli, Sanyal, Amit K.
School: University of California, Davis
Department: Mechanical and Aeronautical Engineering
School Location: United States -- California
Source: DAI-B 74/10(E), Dissertation Abstracts International
Subjects: Aerospace engineering, Mechanical engineering
Keywords: Design optimization, Genetic algorithms, Nonlinear control, Spacecraft control
Publication Number: 3565559
ISBN: 9781303154737