Abstract
In this research, a novel neural-fuzzy guidance law by applying different neural network optimization algorithms alternatively in each step is proposed, such as the Gradient Descent (GD), SCG (Scaled Conjugate Gradient), and Levenberg-Marquardt (LM) methods are applied to deal with those parameter variation effects as follows: target maneuverability, missile autopilot time constant, turning rate time constant and radome slope error effects. Comparing with the proportion navigation (PN) and fuzzy methods are also made; the miss distances obtained by the proposed method are lower, and the proposed acceleration commands are always without polarity changes or oscillation at the final stage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nesline, F.W., Zarchan, P.: Missile Guidance Design Tradeoffs for High-Altitude Air Defense. Journal of Guidance, Control, and Dynamics 6, 207–212 (1983)
Jinho, K., Jungsoon, J.: Nonlinear Model Inversion Control for Bank-to-Turn Missile. In: AIAA Guidance, Navigation and Control Conference, Baltimore, MD (1995)
Mishra, S.K., Sarma, I.G., Swamy, K.N.: Performance Evaluation of Two Fuzzy-Logic-Based Homing Guidance Schemes. Journal of Guidance, Control, and Dynamics 17, 1389–1391 (2006)
Lin, C.L., Hung, H.Z., Chen, Y.Y., Chen, B.S.: Development of an Integrated Fuzzy-Logic-Based Missile Guidance Law Against High Speed Target. IEEE Trans. on Fuzzy Systems 12, 157–169 (2004)
Chen, B.S., Chen, Y.Y., Lin, C.L.: Nonlinear Fuzzy H∞ Guidance Law with Saturation of Actuators Against Maneuvering Targets. IEEE Trans. Control Syst. Technol. 10, 769–779 (2002)
Mitchell, T.M.: Machine Learning. McGraw-Hill (1997)
Andrei, N.: Scaled Memoryless BFGS Preconditioned Conjugate Gradient Algorithm for Unconstrained Optimization. Optimization Methods and Software, 561–571 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lin, JM., Lin, CH. (2014). A Novel Neural-Fuzzy Guidance Law Design by Applying Different Neural Network Optimization Algorithms Alternatively for Each Step. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-05458-2_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05457-5
Online ISBN: 978-3-319-05458-2
eBook Packages: Computer ScienceComputer Science (R0)