Abstract
This paper presents a class of modified Hopfield neural networks and their use in solving aircraft optimal control and identification problems. This class of networks consists of parallel recurrent networks which have variable dimensions that can be changed to fit the problems under consideration. It has a structure to implement an inverse transformation that is essential for embedding optimal control gain sequences. Equilibrium solutions are discussed. Energy minimization of the networks leads to identification of the system parameters. Numerical results are provided to identify the dynamics of an aircraft, and the corresponding optimal control is calculated online. Comparison of the neural network solutions with point-wise optimal control using LQR formulation for this multivariable control problem shows near identical results throughout the trajectories.
Recommended Citation
J. Shen and S. N. Balakrishnan, "A Class of Modified Hopfield Networks for Aircraft Identification and Control," 21st Atmospheric Flight Mechanics Conference, p. 803, American Institute of Aeronautics and Astronautics, Jan 1996.
The definitive version is available at https://doi.org/10.2514/6.1996-3428
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Open Access
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.
Publication Date
01 Jan 1996
