Abstract

A Hopfield neural network architecture is developed to solve the optimal control problem for homing missile guidance. A linear quadratic optimal control problem is formulated in the form of an efficient parallel computing device known as a Hopfield neural network. Convergence of the Hopfield network is analyzed from a theoretical perspective, showing that the network, as a dynamical system approaches a unique fixed point which is the solution to the optimal control problem at any instant during the missile pursuit. Several target-intercept scenarios are provided to demonstrate the use of the recurrent feedback neural net formulation.

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Hopfield Neural Nets; Hopfield Neural Networks; Aerospace Control; Architecture; Dynamical System; Homing Missile Guidance; Linear Quadratic Optimal Control; Missiles; Optimal Control; Optimal Guidance; Parallel Computing; Recurrent Feedback Neural Net; Target-Intercept Scenarios

International Standard Serial Number (ISSN)

0018-9251

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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