Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-09T09:12:26.028Z Has data issue: false hasContentIssue false
  • English
  • Français

A dynamically fuzzy gain – scheduled design for missile autopilot

Published online by Cambridge University Press:  04 July 2016

Chun-Liang Lin  and
Chai-Lin Hwang
Chun-Liang Lin
Affiliation:
Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan
Chai-Lin Hwang
Affiliation:
Chung Shan Institute of Science and Technology, Taoyuan, Taiwan
Get access Rights & Permissions [Opens in a new window]

Abstract

A dynamic backpropagation training algorithm for an adaptive fuzzy gain scheduling feedback control scheme with the application to missile autopilots is developed. This novel design methodology uses a Takagi-Sugeno fuzzy system to represent the fuzzy relationship between the scheduling variables and controller parameters. Mach number and angle-of-attack are used as measured, time-varying exogenous scheduling variables injected into the control law. By incorporating scheduling parameter variation information, the adaptation law for controller parameters is derived. Results from extensive simulation studies show that the presented approach offers satisfactory controlled system performance.

Type
Research Article
Information
The Aeronautical Journal , Volume 107 , Issue 1076 , October 2003 , pp. 599 - 606
Copyright
Copyright © Royal Aeronautical Society 2003 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Rugh, W.J. Analytical framework for gain scheduling, IEEE Control Systems Magazine, 1991, 11, (1), pp 79–84.Google Scholar
2. Tan, S., Hang, C.C. and Chai, J.S. Gain scheduling: from conventional to neuro-fuzzy, Automatica, 1997, 33, (3), pp 411–419.Google Scholar
3. Balas, G.J. and Packard, A.K. Design of robust time-varying controllers for missile autopilot, 1992, Proceedings of the 31st IEEE conference on control applications, Dayton, pp 104–110.Google Scholar
4. Shamma, J.S. and Cloutier, J.R. Trajectory scheduled missile autopilot design, 1992, Proceedings of the 31st IEEE conference on control applications, Dayton, pp 237–242.Google Scholar
5. White, D.P., Wozniak, J.G. and Lawrence, D.A. Missile autopilot design using a gain scheduling technique, 1994, Proceedings of the 26th southeastern symposium on system theory, Athens, OH, pp 606–610.Google Scholar
6. Piou, J.E. and Sobel, K.M. Application of gain scheduling eigenstructure assignment to flight control design, 1996, Proceedings of IEEE International conference on control applications, Dearborn, MI, pp 101–106.Google Scholar
7. Reichert, R.T. Robust autopilot design using µ–synthesis, 1990, Proceedings of American control conference, San Diego, CA, pp 2368–2373.Google Scholar
8. Nichols, R.A., Reichert, R.T. and Rugh, W.J. Gain scheduling for H ∞ controllers: a flight control example, IEEE Transactions on Control Systems Technology, 1993, 1, (2), pp 69–79.Google Scholar
9. Schumacher, C. and Khargonekar, P.P. Missile autopilot designs using H ∞ control with gain scheduling and dynamic inversion, AIAA J Guidance, Control, and Dynamics, 1998, 21, (2), pp 234–243.Google Scholar
10. Shamma, J.S. and Cloutier, J.R. Gain-scheduled missile autopilot design using linear parameter varying transformations, AIAA J Guidance, Control, and Dynamics, 1993, 16, (2), pp 256–263.Google Scholar
11. Apkarian, P., Gahinet, P. and Becker, G. Self-scheduled H ∞ control of linear parameter-varying systems: a design example, Automatica, 1995, 31, (9), pp 1251–1261.Google Scholar
12. Lightbody, G. and Irwin, G.W. Neural model reference adaptive control and application to a BTT-CLOS guidance system, 1994, Proceedings of IEEE International conference on neural networks, Orlando, FL, pp 2429–2435.Google Scholar
13. Lightbody, G. and Irwin, G.W. Direct neural model reference adaptive control, IEE Proc Pt D, 1995, 142, (1), pp 31–43.Google Scholar
14. Devaud, E. and Harcaut, J.P. and Siguerdidjane, H. Three-axes missile autopilot design: from linear to nonlinear strategies, AIAA J Guidance, Control, and Dynamics, 2001, 24, (1), pp 64–71.Google Scholar
15. Takagi, T. and Sugeno, M. Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15, (1), pp 116–132.Google Scholar
16. Driankov, D., Hellendoorn, H. and Reinfrank, M. An Introduction to Fuzzy Control, 1993, Springer, Berlin, pp 186–195.Google Scholar
17. Gonsalves, P.G. and Zacharias, G.L. Fuzzy logic gain scheduling for flight control, 1994, Proceedings of the 3rd IEEE Conference on fuzzy systems, Orlando, FL, pp 952–957.Google Scholar
18. Adams, R.J., Sparks, A.G. and Banda, S.S. A gain scheduled multivariable design for a manual flight control system, 1992, Proceedings of the 31st IEEE Conference on control applications, Dayton, OH, pp 584–589.Google Scholar
19. Tanaka, T. and Y., Aizawa, Y. A robust gain scheduler interpolated into multiple models by membership functions, August 1992, AIAA Paper 92–4553.Google Scholar
20. Jonckheere, E.A., Yu, G.R and Chen, C.C. Gain scheduling for lateral motion of propulsion controlled aircraft using neural networks, 1997, Proceedings of American control conference, Albuquerque, NM, pp 3321–3325.Google Scholar
21. Luenberger, D.G. Optimisation by Vector Space Methods, 1969, John Wiley & Sons, New York, pp 297–299.Google Scholar