Abstract
To advocate the development of new robust and reliable controllers, it has been defined a benchmark problem, where robust controllers are required for controlling a 6-Degree of Freedom (6-DOF) nonlinear F16 aircraft in auto-landing phase undergoing actuator failures. This paper attempt to provide a solution by developing a robust Neural Network (Neuro) aided H2 controller. Simulation results show that the fault tolerant performance of the proposed Neuro-aided H2 controller is better than H2 controller under actuator failure condition, both the number of hidden neurons and the time of on-line learning are small enough to be used in engineer problems.
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
Pallett, E.H.J., Coyle, S.: Automatic Flight Control. Blackwell Scientific (1993)
Donald, M.: Automatic Flight Control Systems. Prentice-Hall (1990)
Bole, M., Svoboda, J.: Design of a Reconfigurable Automated Landing System for VTOL Unmanned Air Vehicles. In: Proceedings of the IEEE 2000 National Aerospace and Electronics Conference, NAECON 2000, pp. 801–806 (2000)
Jorgensen, C.C., Scheley, C.: Neural Network Baseline Problem for Control of Aircraft Flare and Touchdown. In: Neural Networks for Control, pp. 402–425. MIT Press, Cambridge (1990)
Calise, A.J.: Neural Networks in Non-Linear Aircraft Flight Control. IEEE Aerospace and Electronic Systems Magazine, 5–19 (July 1996)
Steinberg, M., Di Giralamo, R.: Applying Neural Network Technology to Future Generation Military Flight Control Systems. In: International Joint Conference on Neural Networks, Seattle, WA (1991)
Iiguni, Y., Akiyoshi, H., Adachi, N.: An Intelligent Landing System Based On a Human Skill Model. IEEE Trans. on Aerospace and Electronic System 34(3), 877–882 (1998)
Napolitano, M.R., Kincheloe, M.: On-Line Learning Neural-Network Controllers for Autopilot Systems. AIAA Journal of Guidance, Control and Dynamics 33(6), 1008–1015 (1995)
Lu, Y., Sundararajan, N., Saratchandran, P.: Performance Evolution of a Sequential Minimal Radial Basis function(RBF) Neural Network Learning Algorithm. IEEE Tran. on Neural Network 9(2), 308–318 (1998)
Cao, Y.Y., Lin, Z.L., Ward, D.G.: Hinf Antiwindup Design for Linear Systems Subject to Input Saturation. Journal of Guidance, Control, and Dynamics 25(3), 455–463 (2002)
Naidu, D.S., Banda, S.S., Buffington, J.L.: Unified Approach To H2 and Hinf Optimal Control of a Hypersonic Vehicle. In: Proc. of the American Control Conference, pp. 2737–2741, San Diego, California (June 1999)
Hyde, R.A., Glover, K.: The Application of Scheduled Hinf Controllers to a VSTOL Aircraft. IEEE Trans. on Automatic Control 38(7), 402–425 (1993)
Nguyen, L.T., Marilyn, E.O., William, P.G., Kemper, S.K., Philip, W.B., Deal, P.L.: Simulator study of stall/post stall characteristics of a fighter airplane with relaxed longitudinal static stability. NASA Technical Paper 1538, USA (1979)
Li, Y., Wang, Z.F., Sundararajan, N.: Robust Neural Controller Design for Aircraft Auto Landing Under Failures., DSOCL01144, Final Report (December 2003)
Che, J., Chen, D.G.: Automatic Landing Control using Hinf Control and Stable Inversion. In: Orlando, F.U. (ed.) Proceedings of the 40th IEEE Conference on Decision and Control (December 2001)
Shue, S., Agarwal, R.K.: Design of Automatic Landing Systems using Mixed H2/Hinf Control. Journal of Guidance, Control and Dynamics 22(1), 103–114 (1999)
Li, Y., Sundararajan, N., Saratchandran, P., Wang, Z.F.: Robust Neuro-Hinf Controller Design for Aircraft Auto-Landing. IEEE Transactions on Aerospace and Electronic Systems 40(1), 167–168 (2004)
Li, Y., Wang, Z.F., Sundararajan, N., Saratchandran, P.: Robust and Adaptive Neuro-Controller for Aircraft Undergoing Microburst. In: Proceedings of the American Control Conference, Denver, Colorado (2003)
Gomi, H., Kawato, M.: Neural network control for a closed-loop system using feedback-error-learning. Neural Networks 6(7), 933–946 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Wang, Z., Xiong, G. (2012). Neuro-aided H2 Controller Design for Aircraft under Actuator Failure. In: Gaol, F., Nguyen, Q. (eds) Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science. Advances in Intelligent and Soft Computing, vol 145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28308-6_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-28308-6_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28307-9
Online ISBN: 978-3-642-28308-6
eBook Packages: EngineeringEngineering (R0)