Adaptive-Critic-Based Neural Networks for Aircraft Optimal Control
Abstract
A dual neural network architecture for the solution of aircraft control problems is presented. The neural network structure, consisting of an action network and a critic network, is used to approximately solve the dynamic programming equations associated with optimal control with a high degree of accuracy. Numerical results from applying this methodology to optimally control the longitudinal dynamics of an aircraft are presented. The novelty in this synthesis of the optimal controller network is that it needs no external training inputs; it needs no a priori knowledge of the form of control. Numerical experiments with neural-network-based control as well as other pointwise optimal control techniques are presented. These results show that this network architecture yields optimal control over the entire range of training. In other words, the neural network can function as an autopilot. A scalar problem is also used in this study for easier illustration of the solution development.
Recommended Citation
S. N. Balakrishnan and V. Biega, "Adaptive-Critic-Based Neural Networks for Aircraft Optimal Control," Journal of Guidance, Control, and Dynamics, American Institute of Aeronautics and Astronautics (AIAA), Jan 1996.
The definitive version is available at https://doi.org/10.2514/3.21715
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
0731-5090
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 1996 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.
Publication Date
01 Jan 1996
