"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.
Balakrishnan, S. N.
Riggins, David W.
Dagli, Cihan H., 1949-
Mechanical and Aerospace Engineering
M.S. in Aerospace Engineering
University of Missouri--Rolla
vii, 42 pages
© 2000 Jonathan Richard Grohs, All rights reserved.
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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.http://merlin.lib.umsystem.edu/record=b4414650~S5
Grohs, Jonathan Richard, "Hypersonic vehicle trajectory optimization using neural networks" (2000). Masters Theses. 1901.
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