Hypersonic vehicle trajectory optimization using neural networks

Author

Jonathan Richard Grohs

Abstract

"In this thesis, the optimal control of a hypersonic vehicle in ascent through the atmosphere is developed using neural networks. Using numerical methods of optimal control and the flexibility and nonlinear mapping ability of neural networks, such a controller of a highly nonlinear system is feasible.

The full nonlinear equations of motion are modeled and along with the control requirements are modified from an open final time time-invariant optimal control problem to a terminal 'time'-variant optimal control problem. A study of the physics of the problem using modified Two Point Boundary Value Problem solutions is undertaken to provide a reference optimal control solution. A modified extension to the Adaptive Critic Neural Network architecture is developed specifically for the structure of this problem. A series of controllers trained through this neural network structure are formed which control the vehicle along an optimal trajectory for a range of initial conditions. These neurocontrollers need no external training, perform with a degree of robustness to the initial conditions and are directly capable of being implemented in feedback control form"--Abstract, page iii.

Advisor(s)

Balakrishnan, S. N.

Committee Member(s)

Riggins, David W.
Dagli, Cihan H., 1949-

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Aerospace Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2000

Pagination

vii, 42 pages

Note about bibliography

Includes bibliographical references (pages 40-41).

Rights

© 2000 Jonathan Richard Grohs, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Thesis Number

T 7717

Print OCLC #

43932689

Electronic OCLC #

1102054741

Link to Catalog Record

Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Grohs, Jonathan Richard, "Hypersonic vehicle trajectory optimization using neural networks" (2000). Masters Theses. 1901.
https://scholarsmine.mst.edu/masters_theses/1901