Design of an anti-overshoot Mamdani-type fuzzy-adaptive controller for yaw angle control of a model helicopter

International Journal of Intelligent Systems Technologies and Applications

Volumn 4, Issue 3-4, 2008, Pages 386-398

  Mohammadzaheri, Morteza ^a   Mirsepahi, Ali ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

anti overshoot; fuzzy control; model helicopter; yaw angle

Indexed keywords

EID: 69549104878     PISSN: 17408865     EISSN: 17408873     Source Type: Journal    
DOI: 10.1504/ijista.2008.017280     Document Type: Article

References (19)

1. Chen, T-T. and Li, T-H.S. (2001) ‘Simplex-type fuzzy sliding-mode control’, Fuzzy Sets and Systems, Vol. 124, pp.249–261.
2. Ghaffari, A., Mehrabian, A.R. and Mohammad-Zaheri, M. (2007) ‘Identification and control of power plant de-super heater using soft computing techniques’, Engineering Applications of artificial Intelligence, Special Issue in Applications of A.I. in Process Engineering, Vol. 20, pp.273–287.
3. Hou, X., Li, P., Fang, Z. and Han, B. (2006) ‘An application of fuzzy PID algorithm on unmanned helicopter attitude control’, The Sixth World Congress on Intelligent Control and Automation, WCICA 2006. Publication Date: 21–23 June 2006, Vol. 2, pp.9129–9133.
4. Jang, J.R., Sun, C. and Mizutani, E. (1997) Neuro-Fuzzy and Soft Computing. Upper Saddle River, NJ: Prentice-Hall Inc.
5. Kadmiry, B. and Driankov, D. (2004) ‘A fuzzy fight controller combining linguistic and model-based fuzzy control’, Fuzzy Sets and Systems, Vol. 146, pp.313–347.
6. Labioda, S., Boucherit, M.S. and Guerra, T.M. (2005) ‘Adaptive fuzzy control of a class of MIMO nonlinear systems’, Fuzzy Sets and Systems, Vol. 151, pp.59–77.
7. Rahbari, R., Leach, B.W., Dillon, J. and de Silva, C.W. (2005) ‘Expert system for an INS/DGPS integrated navigator installed in a Bell 206 helicopter’, Engineering Applications of Artificial Intelligence, Vol. 18, pp.353–361.
8. Rojas, I., Pomares, H., Gonzalez, J., Herrera, L.J., Guillen, A., Rojas, F. and Valenzuela, O. (in press) ‘Adaptive fuzzy controller: application to the control of the temperature of a dynamic room in real time’, Fuzzy Sets and Systems (article in press).
9. Sabatto, Z. and Zheng, S.Y. (1997) ‘Intelligent flight controllers for helicopter control’, International Conference on Neural Networks, 1997, Publication Date: 9–12 June 1997, Vol. 2, pp.617–621.
10. Sanchez, E.N., Becerra, H.M. and Velez, C.M. (2007) ‘Combining fuzzy, PID and regulation control for an autonomous mini-helicopter’, Information Sciences, Vol. 177, pp.1999–2022.
11. Shao, H., Nonami, K., Wojtara, T., Yuasa, R., Amano, S. and Waterman, D. (2006) ‘Neuro-fuzzy position control of demining tele-operation system based on RNN modeling’, Robotics and Computer-Integrated Manufacturing, Vol. 22, pp.25–32.
12. Shen, K., Lu, J., Li, Z. and Liu, G. (2005) ‘An adaptive fuzzy approach for the incineration temperature control process’, Fuel, Vol. 84, pp.1144–1150.
13. Tanaka, K., Othake, H. and Wang, H.O. (2004) ‘A practical design approach to stabilization of a 3-DOF RC helicopter’, IEEE Transactions on Control system Technology, Vol. 12, pp.315–325.
14. Tian, L. and Collins, C. (2005) ‘Adaptive neuro-fuzzy control of a flexible manipulator’, Mechatronics, Vol. 15, pp.1305–1320.
15. Treesatayapun, C. and Uatrongjit, S. (2005) ‘Adaptive controller with fuzzy rules emulated structure and its applications’, Engineering Applications of Artificial Intelligence, Vol. 18, pp.603–615.
16. Unneland, K. (2003) ‘Application of model predictive control to a helicopter model’, Master Thesis Performed at Swiss Federal Institute of Technology in Zurich.
17. Yang, Y. (2005) ‘Direct robust adaptive fuzzy control (DRAFC) for uncertain nonlinear systems using small gain theorem’, Fuzzy Sets and Systems, Vol. 151, pp.79–97.
18. Yang, Y., Luo, A. and Rydberg, K. (2006) ‘PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system’, Journal of Control Theory and Applications, Vol. 4, pp.327–330.
19. Zhou, S., Feng, G. and Feng, C-B. (2005) ‘Robust control for a class of uncertain nonlinear systems: adaptive fuzzy approach based on back stepping’, Fuzzy Sets and Systems, Vol. 151, pp.1–20.