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An optimal-fuzzy two-phase CLOS guidance law design using ant colony optimisation

Published online by Cambridge University Press:  03 February 2016

H. Nobahari  and
S. H. Pourtakdoust
H. Nobahari
Affiliation:
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
S. H. Pourtakdoust
Affiliation:
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
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Abstract

The well-known ant colony optimisation (ACO) meta-heuristic is applied to optimise the parameters of a new fuzzy command to line-of-sight (CLOS) guidance law. The new guidance scheme includes two phases, a midcourse and a terminal phase. In the first phase, a lead strategy is utilised which reduces the acceleration demands. A proportional derivative (PD) fuzzy sliding mode controller is used as the main tracking controller of the first phase. Moreover, a supervisory controller is coupled with the main tracking controller to guarantee the missile flight within the beam. In the terminal phase, a pure CLOS guidance law without lead angle is utilised. For this phase, a new hybrid fuzzy proportional-integral-derivative (PID) fuzzy sliding mode controller is proposed as a high precision tracking controller. The parameters of the proposed controllers for the first and the second phases are optimised using ACO. In this regard, the recently developed continuous ant colony system (CACS) algorithm is extended to multi-objective optimisation problems and utilised to optimise the parameters of the pre-constructed fuzzy controllers. The performance of the resulting guidance law is evaluated at different engagement scenarios and compared with the well-known feedback linearisation method. The comparison is also made in the presence of measurement noise.

Type
Research Article
Information
The Aeronautical Journal , Volume 111 , Issue 1124 , October 2007 , pp. 621 - 636
Copyright
Copyright © Royal Aeronautical Society 2007 

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References

1. Locke, A.S., Guidance, 1955 D Van Nostrand Company, Princeton.Google Scholar
2. Garnell, P., Guided Weapon Control Systems, 1980, Second edition, Pergamon Press, Oxford, UK.Google Scholar
3. Zarchan, P., Tactical and Strategic Missile Guidance, 1997, Third edition, AIAA Education Series, Vol 176, AIAA.Google Scholar
4. Blackelock, J.H., Automatic Control of Aircraft and Missiles, 1991, Second edition, New York, Wiley.Google Scholar
5. Shneydor, N.A., Missile Guidance and Pursuit: Kinematics, Dynamics and Control, Horwood Publishing, Chichester, England, UK, 1998.Google Scholar
6. Kain, J.E. and Yost, D.J., Command to line-of-sight guidance: a stochastic optimal control problem, AIAA Guidance and Control conference proceedings, 1976, pp 356–364.Google Scholar
7. Ha, I.J. and Chong, S.. Design of a CLOS Guidance law via feedback linearization, 1992, IEEE Transactions on Aerospace and Electronic Systems, 28, (1), pp 51–62.Google Scholar
8. Parkes, N.E. and Roberts, A.P.. Application of polynomial methods to design of controllers for CLOS guidance, 1994, IEEE Conference on Control Applications, 2, pp 1453–1458.Google Scholar
9. Benshabat, D.G. and Bar-Gill, A.. Robust command-to-line-of-sight guidance via variable-structure control, 1995, IEEE Transactions on Control Systems Technology, 3, (3), 1995, pp 356–361.Google Scholar
10. Pourtakdoust, S.H. and Nobahari, H., Optimization of LOS guidance for surface-to-air missiles, September 2000, Proceedings of Iranian Aerospace Organization Conference, Tehran, pp 245–257.Google Scholar
11. Nobahari, H., Alasty, A. and Pourtakdoust, S.H.. Design of a supervisory controller for CLOS guidance with lead angle, AIAA Guidance, Navigation and Control Conference and Exhibition, AIAA 2005-6156, pp 1–13.Google Scholar
12. Arvan, M.R. and Moshiri, B.. Optimal fuzzy controller design for an anti-tank missile, September 1996, Proceedings of International Conference on Intelligent and Cognitive Systems, Tehran, pp 123–128.Google Scholar
13. Lin, C.M. and Mon, Y.J.. Fuzzy-logic-based CLOS guidance law design, 2001, IEEE Transactions on Aerospace and Electronic Systems, 37, (2), pp 719–727.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. Lin, C.M., Hsu, C.F. and Mon, Y.J.. Self-organizing fuzzy learning CLOS guidance law design, 2003, IEEE Transactions on Aerospace and Electronic Systems, 39, (4), pp 1144–1151.Google Scholar
16. Nobahari, H. and Pourtakdoust, S.H.. Optimal fuzzy CLOS guidance law design using ant colony optimization, 2005, Lecture Notes in Computer Science, 3777, pp 95–106.Google Scholar
17. Lee, G.T. and Lee, J.G., Improved command-to-Line-of-sight for homing guidance, IEEE Transactions on Aerospace and Electronic Systems, 1995, 31, (1), pp 506–510.Google Scholar
18. Jalali-Naini, S.H. and Esfahanian, V.. Closed-form solution of Line-of-sight trajectory for non-maneuvering targets, J Guidance, Control and Dynamics, 2000, 23, (2), pp 365–366.Google Scholar
19. Jalali-Naini, S.H.. Analytical study of a modified LOS guidance, AIAA Guidance, Navigation and Control Conference, AIAA 2001-4045, pp 1–8.Google Scholar
20. Jalali-Naini, S.H.. Generalized Line-of-sight guidance with lead angle, Iranian Journal of Science and Technology, Transaction B, 2004, 28, (B4), pp 489–493.Google Scholar
21. Belkhouche, F., Belkhouche, B. and Rastgoufard, P.. A Linearized model for the Line-of-sight guidance law, 2004, Proceedings of IEEE Position Location and Navigation Symposium, pp 201–207.Google Scholar
22. Wang, L.X., A supervisory controller for fuzzy control systems that guarantees stability, 1994, IEEE Transaction on Automatic Control, 40, (1), pp 1845–1848.Google Scholar
23. Palm, R.. Robust control by fuzzy sliding mode, Automatica, 1994, 30, (9), pp 1429–1437.Google Scholar
24. Chen, C.L. and Chang, M.H.. Optimal design of fuzzy sliding-mode control: a comparative study, Fuzzy Sets and Systems, 1998, 93, pp 37–48.Google Scholar
25. Choi, B.J., Kwak, S.W. and Kim, B.K.. Design of a single-input fuzzy logic controller and its properties, Fuzzy Sets and Systems, 1999, 106, (3), pp 299–308.Google Scholar
26. Brehm, T. and Rattan, K.S.. Hybrid fuzzy logic PID controller, Proceedings of the IEEE National Aerospace and Electronics Conference, 1993, 2, pp 807–813.Google Scholar
27. Zhao, Z.Y., Tomizuka, M. and Isaka, S., Fuzzy gain scheduling of PID controllers, 1993, IEEE Trans. Systems, Man and Cybernetics, 23, pp 1392–1398.Google Scholar
28. Procyk, T.J. and Mamdani, E.H.. A Linguistic self-organizing process controller, Automatica, IFAC, 1979, 15, pp 15–30.Google Scholar
29. Takagi, T. and Sugeno, M.. Fuzzy identification of systems and its application to modeling and control, 1985, IEEE Trans. Systems, Man and Cybernetics, 15, pp 116–132.Google Scholar
30. Nomura, H., Hayashi, I. and Wakami, N.. A Self tuning method of fuzzy control by descent method, Engineering, 1991, Bruxelles, Proceedings of the International Fuzzy Systems Association, IFSA91, pp 155–158.Google Scholar
31. Siarry, P. and Guely, F.. A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases, Fuzzy Sets and Systems, 1998, 99, pp 37–47.Google Scholar
32. Nobahari, H. and Pourtakdoust, S.H.. Optimization of fuzzy rule bases using continuous ant colony system, 2005, Proceeding of the First International Conference on Modeling, Simulation and Applied Optimization, Sharjah, UAE, 243, pp 1–6.Google Scholar
33. Pourtakdoust, S.H. and Nobahari, H.. An extension of ant colony system to continuous optimization problems, Lecture Notes in Computer Science, 2004, 3172, pp 294–301.Google Scholar
34. Colorni, A., Dorigo, M. and Maniezzo, V.. Distributed optimization by ant colonies, 1992, Proceedings of the First European Conference on Artificial Life, pp 134–142.Google Scholar
35. Dorigo, M. and Gambardella, L.M.. Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, 1997, 1, (1), pp 53–66.Google Scholar
36. Slotine, J.J.E. and Li, W., Applied Nonlinear Control, 1991, Prentice-Hall, Upper Saddle River, NJ.Google Scholar
37. Rao, S.S., Engineering Optimization, 1996, John Wiley & Sons and New Age International (P).Google Scholar