Abstract
In this paper, the reinforcement learning technique is proposed to implement evasive strategies for aircrafts during engagement. A simplified point-mass model is used to describe the aircraft and the missile equations of motion. The missile follows the pure proportional navigation guidance (PPNG) law to attack the aircraft. Q-learning algorithm which is a form of reinforcement learning is suggested to learn the evasive maneuvers. The performance of the proposed approach is analyzed with numerical simulations. It is shown that the aircraft evades from a missile properly by reinforcement learning with bang-bang type action profiles.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ben-Asher, J., Cliff, E.M., Kelley, H.J.: Optimal evasion against a proportional guided pursure. Journal of Guidance, Control, and Dynamics 12(4), 598–600 (1989)
Imado, F., Miwa, S.: Fighter evasive maneuvers against proportional navigation missile. Journal of Aircraft 23(11), 825–830 (1986)
Ong, S.Y., Pierson, B.L.: Optimal planar evasive aircraft maneuvers against proportional navigation missiles. Journal of Guidance, Control, and Dynamics 19(6), 1210–1215 (1996)
Watkins, C.: Learning from delayed rewards, PhD. Thesis, Cambridge, England (1989)
Manju, M.S.: An analysis of Q-learning algorithms with strategies of reward function. International Journal on Computer Science and Engineering 3(2), 814–820 (2011)
Yang, C.D., Yang, C.C.: Optimal pure proportional navigation for maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems 33, 949–957 (1997)
Raivio, T.: Capture set computation of an optimal guided missile. Journal of Guidance, Control, and Dynamics 24(6), 1167–1175 (2001)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. The MIT Press (1998)
Lee, D., Bang, H., Baek, K.: Autorotation of an Unmanned Helicopter by a Reinforcement Learning Algorithm. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hawaii (2008)
Jung, B., Kim, K.S., Kim, Y.: Guidance law for evasive aircraft maneuvers using artificial intelligence. In: Guidance, Navigation, and Control Conference and Exhibit, Austin, Texas (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lee, D., Bang, H. (2013). Planar Evasive Aircrafts Maneuvers Using Reinforcement Learning. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-33926-4_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33925-7
Online ISBN: 978-3-642-33926-4
eBook Packages: EngineeringEngineering (R0)