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.
Recommended Citation
S. N. Balakrishnan and J. E. Steck, "Use of Hopfield Neural Networks in Optimal Guidance," IEEE Transactions on Aerospace and Electronic Systems, Institute of Electrical and Electronics Engineers (IEEE), Jan 1994.
The definitive version is available at https://doi.org/10.1109/7.250431
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.
Publication Date
01 Jan 1994
