Abstract
The purpose of this paper is to investigate the use of evolutionary fuzzy neural systems to aircraft automatic landing control and to make the automatic landing system more intelligent. Three intelligent aircraft automatic landing controllers are presented that use fuzzy-neural controller with BPTT algorithm, hybrid fuzzy-neural controller with adaptive control gains, and fuzzy-neural controller with particle swarm optimization, to improve the performance of conventional automatic landing system. Current flight control law is adopted in the intelligent controller design. Tracking performance and adaptive capability are demonstrated through software simulations.
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
Buschek, H., Calise, A.J.: Uncertainty Modeling and Fixed-Order Controller Design for a Hypersonic Vehicle Model. Journal of Guidance, Control, and Dynamics 20, 42–48 (1997)
Federal Aviation Administration: Automatic Landing Systems. AC 20-57A (1971)
Boeing Publication: Statistical Summary of commercial Jet Airplane Accidents. Worldwide Operations 1959-1999 (2000)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1945–1950 (1999)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 84–89 (1998)
Zheng, Y.L., Ma, L., Zhang, L., Qian, J.: On the Convergence Analysis and Parameter Selection in Particle Swarm Optimization. In: Proceedings of the Second IEEE International Conference on Machine Learning and Cybernetics, pp. 1802–1807 (2003)
Izadi, H., Pakmehr, M., Sadati, N.: Optimal Neuro-Controller in Longitudinal Autolanding of a Commercial Jet Transport. In: Proceedings of IEEE International Conference on Control Applications, pp. 1–6 (2003)
Chaturvedi, D.K., Chauhan, R., Kalra, P.K.: Application of Generalized Neural Network for Aircraft Landing Control System. Soft Computing 6, 441–448 (2002)
Ionita, S., Sofron, E.: The Fuzzy Model for Aircraft Landing Control. In: Proceedings of AFSS International Conference on Fuzzy Systems, pp. 47–54 (2002)
Nho, K., Agarwal, R.K.: Automatic Landing System Design Using Fuzzy Logic. Journal of Guidance, Control, and Dynamics 23, 298–304 (2000)
Jorgensen, C.C., Schley, C.: A Neural Network Baseline Problem for Control of Aircraft Flare and Touchdown. Neural Networks for Control, 403–425 (1991)
Juang, J.G., Chang, H.H., Chang, W.B.: Intelligent Automatic Landing System Using Time Delay Neural Network Controller. Applied Artificial Intelligence 17, 563–581 (2003)
Juang, J.G.: Fuzzy Neural Networks Approaches for Robotic Gait Synthesis. IEEE Transactions on Systems Man and Cybernetics—Part B: Cybernetics 30, 594–601 (2000)
Horikawa, S., Furuhashi, T., Uchikawa, Y.: On Fuzzy Modeling Using Fuzzy Neural Networks with the Back-Propagation Algorithm. IEEE Transactions on Neural Networks 3, 801–806 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Juang, JG., Lin, BS., Chin, KC. (2005). Intelligent Fuzzy Systems for Aircraft Landing Control. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_105
Download citation
DOI: https://doi.org/10.1007/11539506_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
eBook Packages: Computer ScienceComputer Science (R0)