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Genetic Fuzzy Attitude State Trajectory Optimization for a 3U CubeSat

Walker, Alex R
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593265983802031

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
A novel approach to parameterize and solve for optimal satellite attitude state trajectories is presented. The optimal trajectories are parameterized using fuzzy inference systems (FISs), and the FISs are optimized using a genetic algorithm. Eight different constrained optimization problems are solved. The objective of each optimization problem is either battery charge maximization, link margin (equivalent to antenna gain) maximization, or experiment temperature minimization. All optimization problems consider reaction wheel angular velocity and reaction wheel angular acceleration constraints, and five of the optimization problems consider either battery charge constraints, antenna gain constraints, or both battery charge and antenna gain constraints. Reaction wheel constraints are satisfied using an attitude state filter at the output of the FISs and an optimal magnetic torque / reaction wheel desaturation algorithm, the design of both of which is presented herein. Optimal attitude state trajectory, or attitude profile, FISs are compared with a nominal attitude profile. It is shown that, while the nominal attitude profile offers good performance with respect to both battery charge and link margin, the optimal attitude profile FISs are able to outperform the nominal profile with respect to all objectives, and a minimum temperature attitude profile FIS is able to achieve average experiment temperatures 30–40 K lower than the nominal attitude profile. The attitude state trajectory optimization solutions presented in this work are motivated by the needs and constraints of the CryoCube-1 mission. Because this work is integral to the functionality of the CryoCube-1 satellite system, the effort taken to successfully build, test, deliver, launch, and deploy this CubeSat is detailed. The intent of providing this systems view is to provide the context necessary to understand exactly how the attitude state trajectory optimization results were used within the satellite system.
Kelly Cohen, Ph.D. (Committee Chair)
Manish Kumar, Ph.D. (Committee Member)
Ou Ma, Ph.D. (Committee Member)
Phil Putman, Ph.D. (Committee Member)
Anoop Sathyan, Ph.D. (Committee Member)
214 p.
Aerospace Materials
attitude control; fuzzy logic; genetic algorithm; trajectory optimization; optimal control; multidisciplinary

Recommended Citations

Citations

  • Walker, A. R. (2020). Genetic Fuzzy Attitude State Trajectory Optimization for a 3U CubeSat [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593265983802031

    APA Style (7th edition)

  • Walker, Alex. Genetic Fuzzy Attitude State Trajectory Optimization for a 3U CubeSat. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593265983802031.

    MLA Style (8th edition)

  • Walker, Alex. "Genetic Fuzzy Attitude State Trajectory Optimization for a 3U CubeSat." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593265983802031

    Chicago Manual of Style (17th edition)

ucin1593265983802031
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© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.