Abstract
We investigate the use of an `adaptive critic' based controller to steer an agile missile with a constraint on the angle of attack from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. We use neural networks with a two-network structure called `adaptive critic' to carry out the optimization process. This structure obtains an optimal controller through solving Hamiltonian equations. This approach needs no external training; each network along with the optimality equations generates the output for the other network. When the outputs are mutually consistent, the controller output is optimal. Though the networks are trained off-line, the resulting control is a feedback control
Recommended Citation
D. Han and S. N. Balakrishnan, "Adaptive Critic Based Neural Networks for Control-Constrained Agile Missile Control," Proceedings of the 1999 American Control Conference, 1999, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at https://doi.org/10.1109/ACC.1999.786536
Meeting Name
1999 American Control Conference, 1999
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Hamiltonian Equations; Adaptive Critic Based Neural Networks; Angle of Attack; Control-Constrained Agile Missile Control; Feedback; Feedback Control; Flightpath Angle; Missile Control; Neurocontrollers; Optimal Control; Optimisation; Two-Network Structure
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 1999
