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Robust and computational efficient autopilot design: A hybrid approach based on classic control and genetic-fuzzy sliding mode control

Published online by Cambridge University Press:  27 January 2016

A. R. Babaei ,
M. Mortazavi  and
M. B. Menhaj
A. R. Babaei*
Affiliation:
Aerospace Engineering Department, Amirkabir University of Technology, Tehran, Iran
M. Mortazavi
Affiliation:
Aerospace Engineering Department, Amirkabir University of Technology, Tehran, Iran
M. B. Menhaj
Affiliation:
Aerospace Engineering Department, Amirkabir University of Technology, Tehran, Iran
*
[email protected]
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Abstract

The purpose of this paper is developing an efficient flight control strategy in terms of time response characteristics, robustness with respect to both parametric uncertainties and un-modeled nonlinear terms, number of required measurements, and computational burden. The proposed method is based on combination of a classic controller as principal section of the autopilot and a multi-objective genetic algorithm-based fuzzy output sliding mode control (FOSMC). FOSMC not only modifies robustness of the classic controller against uncertainties and external disturbances, but also modifies its time response for wide range of commands. FOSMC is a single input-single output controller that is based on the system output instead of the system states. In this situation, the proposed autopilot does not require measurement of other variables and observer, and also it is practicable because of considerable reduction in rule inferences then computational burden. As a critical application, the proposed method is applied to design the altitude hold mode autopilot for an UAV which is non-minimum phase, uncertain, and nonlinear.

Type
Research Article
Information
The Aeronautical Journal , Volume 117 , Issue 1194 , August 2013 , pp. 839 - 859
Copyright
Copyright © Royal Aeronautical Society 2013 

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References

1. Barkana, I. Classical and simple adaptive control for non-minimum phase autopilot design, J Guidance, Control and Dynamics, 2005, 28, (4), pp 631–638.Google Scholar
2. Bossert, D.E. and Cohen, K. PID and fuzzy logic pitch attitude hold systems for a fghter jet, 2002, AIAA, Guidance, Navigation, and Control Conference and Exhibition, Monterey, CA, USA.Google Scholar
3. Cohen, K. and Bossert, D.E. Fuzzy logic non-minimum phase autopilot design, 2003, AIAA, Guidance, Navigation, and Control Conference and Exhibition, 11-14 August 2003, Austin, TX, USA.Google Scholar
4. Babaei, A.R., Mortazavi, M. and Moradi, M.H. Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles, Applied Soft Computing J, 2011, 11, (1), pp 365–372.Google Scholar
5. Slotine, J.J.E. and Li, W. Applied Nonlinear Control, 1991, Prentice-Hall, Englewood Cliffs, NJ, USA.Google Scholar
6. Kadmiry, B. and Driankov, D. A Fuzzy fight controller combining linguistic and model-based fuzzy control, Fuzzy Sets and Systems, 2004, 146, (4), pp 313–347.Google Scholar
7. Wu, S.F., Engelen, C.J.H., Babuska, R., Chu, Q.P. and Mulder, J.A. Fuzzy logic based full-envelope autonomous fight control for an atmospheric re-entry spacecraft, Control Engineering Practice, 2003, 11, (1), pp 11–25.Google Scholar
8. Blumel, A.L., Hughes, E.J. and White, B.A. Multi-objective evolutionary design of fuzzy autopilot controller, 2001, First International Conference on Evolutionary Multi-Criterion Optimization, pp 668–680.Google Scholar
9. Li, T.H.S. and shieh, M.Y. Design of a GA-based PID controller for non-minimum phase system, Fuzzy Sets and Systems, 2000, 111, (2), pp 183–197.Google Scholar
10. Omar, H.M. Genetic-based fuzzy logic controller for satellites stabilized by reaction wheels and gravity gradient, 2007, AIAA, Guidance, Navigation and Control Conference and Exhibition, Hilton Head, SC, USA.Google Scholar
11. Serra, G.L.O. and Bottura, C.P. Multiobjective evolution based fuzzy PI controller design for nonlinear systems, Eng Applications of Artificial Intelligence, 2006, 19, (2), pp 157–167.Google Scholar
12. Tsourds, A., Hughes, E.J. and White, B.A. Fuzzy multi-objective design for a lateral missile autopilot, Control Engineering Practice, 2006, 14, (5), pp 547–561.Google Scholar
13. Wu, D. and Tan, W.W. Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, Eng Application of Artificial Intelligence, 2006, 19, (8), pp 829–841.Google Scholar
14. Lin, C.M. and Hsu, C.F. Guidance Law design by adaptive fuzzy sliding-mode control, J Guidance, Control and Dynamics, 2002, 25, (2), pp 248–256.Google Scholar
15. Li, J.H., Li, T.H.S. and Ou, T.H. Design and implementation of fuzzy sliding-mode controller for a wedge balancing system, J Intelligent and Robotics Systems, 2003, 37, (3), pp 285–306.Google Scholar
16. Liu, D., Yi, J., Zhao, D. and Wang, W. Adaptive sliding mode fuzzy control for a two-dimensional overhead crane, Mechatronics, 2005, 15, (5), pp 505–522.Google Scholar
17. Sebastian, E. and Sotelo, M.A. Adaptive fuzzy sliding mode controller for the kinematic variables of an underwatervehicle, J Intelligent and Robotics Systems, 2007, 49, (2), pp 189–215.Google Scholar
18. Huang, Y.J., Chang, S.H. and Kuo, T.C. Robust fuzzy output sliding control without the requirement of state measurement, J Intelligent and Robotics Systems, 2008, 53, (2), pp 169–182.Google Scholar
19. Jafarov, E.M. and Tasaltin, R. Design of robust autopilot-output integral sliding mode controllers for guided missile systems with parameter perturbations, Aircraft Eng and Aerospace Tech, 73, (1), pp 16–26.Google Scholar
20. Shtessel, Y.B., Shkolnikov, I.A. and Levant, A. Guidance and control of missile interceptor using second-order sliding mode, IEEE Transactions on Aerospace and Electronic Systems, 45, (1), 2009, pp 110–124.Google Scholar
21. Ishaque, K., Abdullah, S.S., Ayob, S.M. and Salam, Z. Single input fuzzy logic controller for unmanned underwater vehicle, J Intelligent and Robotic Systems, 2010, 59, (1), pp 87–100.Google Scholar
22. Nelson, R.C. Flight Stability and Automatics Control, 1998, McGraw-Hill.Google Scholar
23. Wang, L.X. A Course in Fuzzy Systems and Control, 1997, Upper Saddle River, Prentice-Hall, NJ, USA.Google Scholar
24. Haupt, R.L. and Haupt, S.E. Practical Genetic Algorithm, 2004, John Wiley & Sons, NJ, USA.Google Scholar