Abstract
A Hamiltonian based adaptive critic structure is proposed for solving missile guidance problems. This structure consists of a supervisor neural network called 'critic' and a controller network called 'action'. Together they are used to solve model-based guidance problems. The advantage of this approach is that: i) the training data for each network is created by the other network, ii) the converged solutions yield near optimal guidance over the entire span of the training range, and iii) they can be used as feedback controllers though trained off-line. A main contribution is that we establish a direct link between traditional optimal control formulation and neural networks. The resultant network can act as a repository of gains and its structure is very general. Numerical simulations through an illustrative scalar problem and a typical target-intercept problem demonstrate the potential of the approach.
Recommended Citation
S. N. Balakrishnan and J. Shen, "Hamiltonian based Adaptive Critics for Missile Guidance," 1996 Guidance, Navigation, and Control Conference and Exhibit, pp. 1 - 7, American Institute of Aeronautics and Astronautics, Jan 1996.
The definitive version is available at https://doi.org/10.2514/6.1996-3836
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
