Masters Theses

Abstract

"An algorithm based on the mechanics of natural genetics is used to solve two different optimization problems. The algorithm combines survival of the fittest among string structures with randomized information exchange to form a search algorithm. Initiated with a population of bits-coded individuals, the genetic algorithm searches for the optimal solutions generation-by-generation. The three main operations in the genetic algorithm: Reproduction, Crossover, and Mutation give this algorithm the power of searching. In the first part of this thesis, the genetic algorithm is used to determine the optimal trajectories of the aeroassisted vehicle reentry problem. The trajectories to be optimized are determined not only by the parameter searching but also under a height constraint which make this study more interesting.

In the second part of this thesis the genetic algorithm is used for designing three-axis bang-bang controllers for the time-optimal rigid spacecraft reorientation problem. The firing times of the bang-bang controller and the time of rotation are determined by searching for the angular velocity thresholds and the reorientation time"--Abstract, page iv.

Advisor(s)

Balakrishnan, S. N.

Committee Member(s)

Koval, Leslie Robert
Grow, David E.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Aerospace Engineering

Publisher

University of Missouri--Rolla

Publication Date

Summer 1994

Journal article titles appearing in thesis/dissertation

Suboptimal atmospheric trajectory design using genetic algorithms with variable mutation

A variable mutation genetic algorithm for time-optical spacecraft reorientation controller design

Pagination

xii, 116 pages

Note about bibliography

Includes bibliographical references (page 82).

Rights

© 1994 Yao-Feng Cheng, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Library of Congress Subject Headings

Space vehicles -- Control systems -- Computer simulation
Trajectory optimization
Genetic algorithms

Thesis Number

T 6813

Print OCLC #

31458909

Electronic OCLC #

904019461

Comments

Electronic access to the full-text of this document is restricted to Missouri S&T users. Print thesis not available at Missouri S&T Library.

Print thesis lost & withdrawn; scan made from microfilm is best quality available; there is no page ii.

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