×

Selection and setting of an intelligent fuzzy regulator based on nonlinear model simulations of a helicopter in hover. (English) Zbl 1180.93010

Summary: Most research on fuzzy regulators has focused on the integrating rules in intelligent control systems. This paper evaluates a fuzzy helicopter regulator for a single-rotor PZL Kania helicopter. Unlike other models which only match stable flight ability, the model presented in this paper attempts to match the links between disturbances and hover conditions. Two simulations were performed to validate the model. In the first simulation, a helicopter was evaluated in a fixed hover position. In the second simulation, model robustness was validated by introducing wind gust. Results, both with the initial and with the modified model demonstrated the viability of the proposed regulator.

MSC:

93A30 Mathematical modelling of systems (MSC2010)
93C42 Fuzzy control/observation systems
93C95 Application models in control theory
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Amaral, T.G.B. and Crisostomo, M.M., ”Automatic helicopter motion control using fuzzy logic,” in Proc. of the 10th IEEE International Fuzzy Systems Conference 2001, pp. 860-863, 2001.
[2] Amaral, T.G.B., Crisostomo, M.M. and Pires, V.F., ”Helicopter motion control using adaptive neuro-fuzzy inference controller,” in Proc. of the IEEE Conference on the Industrial Electronics Society 2002, pp. 2090-2095, 2002.
[3] Babuska, R. and Vebruggen, H.B., ”An overview of fuzzy modeling for control,” Control Eng. Practice 4, pp. 1593-1606, 1996. · doi:10.1016/0967-0661(96)00175-X
[4] Chen, T. and Li, T., ”Simplex-type fuzzy sliding-mode control,” Fuzzy Sets and Systems 124, pp. 249-261, 2001. · Zbl 1001.93044 · doi:10.1016/S0165-0114(00)00116-0
[5] Goff, D.A., Thomas, S.M., Jones R.P., Massey C.P. ”A Neural Network Approach to Predicting Airspeed in Helicopters,” Neural Computing & Applications 9, pp. 73-82, 2000. · Zbl 02177974 · doi:10.1007/s005210070018
[6] Henzinger, T.A., Kirsch, C.M., Sanvido, M.A.A. and Pree, W., ”From control models to real-time code using Giotto,” IEEE Control Systems Magazine 23, pp. 50-64, 2003. · doi:10.1109/MCS.2003.1172829
[7] Hess, R.A. and Gao, C., ”A generalized Algorithm for Inverse Simulation Applied to Helicopter Maneuvering Flight,” Journal of the American Helicopter Society 38, pp. 3-15, 1993. · doi:10.4050/JAHS.38.3
[8] Hirota, K., International Application of Fuzzy Technology, Springer-Verlag, 1993. · Zbl 0787.00026
[9] Isidori, A., Markoni, L. and Serrani, A., ”Robust nonlinear motion control of a helicopter,” IEEE Transactions on Automatic Control 48, pp. 413-426, 2003. · Zbl 1364.93535 · doi:10.1109/TAC.2003.809147
[10] Kadmiry, B. and Driankov, D., ”A fuzzy flight controller combining linguistic and model-based fuzzy control,” Elsevier Fuzzy Sets and Systems 146, pp. 313-347, 2004. · Zbl 1055.93526 · doi:10.1016/j.fss.2003.07.002
[11] Kadmiry, B. and Driankov, D., ”A fuzzy gain-scheduler for the attitude control of an unmanned helicopter,” IEEE Transactions on Fuzzy Systems 12, pp. 502-515, 2004. · Zbl 05452532 · doi:10.1109/TFUZZ.2004.832539
[12] Kohn-Rich, S. and Flashner, H., ”Robust fuzzy logic control of mechanical systems,” Elsevier Fuzzy Sets and Systems 133, pp. 77-108, 2003. · Zbl 1022.93030 · doi:10.1016/S0165-0114(02)00212-9
[13] Khosla, R., Engineering Intelligent Hybrid Multi-Agent Systems, Kluwer Academic Publishers, 1997. · Zbl 0891.68113
[14] Kowaleczko, G. and Dzygadlo, Z., ”Analysis of Spatial Motion Dynamics of a Helicopter for Various Models of The Inducted Velocity Field,” Journal of Technical Physics 34, pp. 119-143, 1993.
[15] Król, D., Lower, M. and Szlachetko, B., ”Fuzzy flight control system for helicopter intelligence in hover,” in Proc. of the 5th International Conference on Intelligent Systems Design and Applications 2005, pp. 370-374, 2005.
[16] Król, D., Lower, M. and Szlachetko, B., ”Building the fuzzy control system based on the pilot knowledge,” Lecture Notes in Artificial Intelligence 3683, pp.1373-1379, 2005.
[17] Król, D., Lower, M. and Szlachetko, B., ”Helicopter Intelligence in Hover Quality Improvement of the Fuzzy Regulator,” in Proc. of the 6th International Conference on Intelligent Systems Design and Applications 2006, pp. 326-331, 2006.
[18] Lower, M., Fuzzy Logic Control of a Non-Linear Object Related to a Helicopter, Ph.D. Thesis (in Polish), Wroclaw University of Technology, 2000.
[19] Mamdani, H., ”Advances in the linguistic synthesis of fuzzy controllers,” Int. J. Man-Machine Studies 8, pp. 669-678, 1976. · Zbl 0344.68028 · doi:10.1016/S0020-7373(76)80028-4
[20] McLean, D., Automatic Flight Control Systems, Prentice Hall, 1990. · Zbl 0748.93001
[21] Chang, D.J., Seung, I.S., Sang, K.K., Phillips, C., Karr, C.L., and Walker, G., ”Helicopter Flight Control with Fuzzy Logic and Genetic Algorithms,” Engineering Applications of Artificial Intelligence 9, pp. 175-184 1996. · doi:10.1016/0952-1976(95)00008-9
[22] Raffel, M., Richard, H., Ehrenfried, K., Vander Wall, B., Burley, C., Beaumier, P., McAlister, K. and Pengel,K., ”Recording and evaluation methods of PIV investigations on a helicopter rotor model,” Experiments in Fluids 36, pp. 146-156, 2004. · doi:10.1007/s00348-003-0689-7
[23] Sabatto, Z. and Zheng, ”Intelligent flight controllers for helicopter control,” in Proc. of the International Conference on Neural Networks 1997, pp.617-621, 1997.
[24] Sasaki, M., Ishida, H., Katsuno, T. and Ogasawara, A., ”Learning fuzzy logic controller for hovering a helicopter,” in Proc. of the Conference on Fuzzy Information Processing Society 1998, pp. 25-28, 1998.
[25] Shim, H., Koo, T.J., Hoffmann, F. and Sastry, S., ”A comprehensive study of control design for an autonomous helicopter,” in Proc. of the 37th IEEE Conference on Decision and Control 1998, pp. 3653-3658, 1998.
[26] Tischler, M.B., Colbourne, J.D., Morel, M.R. and Biezad D.J., ”A multidisciplinary flight control development environment and its application to a helicopter.” IEEE Control Systems Magazine 19, pp. 22-33 1999. · doi:10.1109/37.777786
[27] Yu, G.R., ”Nonlinear optimal control of helicopter using fuzzy gain scheduling,” IEEE International Conference on Systems, Man and Cybernetics 4, pp. 3313-3318, 2005. · doi:10.1109/ICSMC.2005.1571657
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.