Document Type : Research Paper

Authors

1 Department of Aerospace Engineering, Khajeh Nasir al-Din Tusi University of Technology, Tehran, Iran

2 Department of Electrical and Computer Engineering, Khajeh Nasir al-Din Tusi University of Technology, Tehran, Iran

Abstract

Modeling and analyzing systems, especially in complex systems with high dynamics, noise and uncertainty in understanding the behavior of systems and decision making is very important problem from long time ago. This paper shows that neuro-fuzzy systems can be used effectively to design the solar arrays of electrical power subsystem of a remote sensing satellite in conceptual design phase. In the design of neuro-fuzzy system, Takagi-Sugeno inference system, hybrid training algorithm and Gaussian membership functions are used. The simulation results obtained in this modeling have an accurate accuracy compared to the experimental data and classical calculations of remote sensing satellites.

Keywords

[1] Wertz, J. R., Everett, D.F., Puschell, J.J., Space Mission Engineering: The New SMAD, Microcosm Press, 2011.
[2] Larson, W. J., Kirkpatrick, D., Sellers, J. J., Thomas, L.D., Verma, D., Applied Space Systems Engineering, Space Technology Series, 2009.
[3] Ley, W., Wittmann, K., Hallmann, W., Handbook of Space Technology, John Wiley and Sons, 2008.
[4] Pisacane, V.L., Fundamentals of Space Systems, Oxford University Press, 2005.
[5] Wang, L.X., A Course in Fuzzy Systems and Control, Prentice Hall International Inc., 1997
[6] Wang, W. Ismail, F. and Golnaraghi, A.F. “A Neuro-fuzzy Approach to Gear Systemmonitoring,” IEEE Transactions on Fuzzy Systems, Vol. 12, No. 5, 2004, pp. 710–723.
[7] Kim, D., Seo, S.J. and Park, G.T., Zero-Moment Point Trajectory Modeling of A Bipedwalking Robot Using An Adaptive Neuro-Fuzzy System, IEEE Proceedings ControlTheory and Applications, Vol. 152 No. 4, 2005, pp. 411–426.
[8] Raad, R. and Raad, I., Neuro-Fuzzy Admission Control in Cellular Networks Inc., Proceedings of 10th IEEE International Conference on Communication Systems, Singapore, 2006, pp. 1–7.
[9] Marza, D. Seyyedi, L.F., Capretz, “Estimation Development Time of Softwareprojects Using A Neuro Fuzzy Approach,” World Academy of Science, Engineering and Technology, Vol. 22 2008, pp. 575–579.
[10] Topalov, A.V., Kayacan, E., Oniz and Y., Kaynak, O., Adaptive Neuro-Fuzzy Controlwith Sliding Mode Learning Algorithm: Application to Antilock Braking System, 7th Asian Control Conference, Hong Kong, China, 2009, pp. 784–789.
[11] Topalov, A.V., Kayacan, E., Oniz, Y. and Kaynak, O., “Neuro-fuzzy Control of antilock Braking System Using Variable-Structure-Systems Based Learning Algorithm,” International Conference on Adaptive and Intelligent Systems, 2009, pp. 166–171.
[12] Topalov, A.V., Oniz, Y., Kayacan, E. and Kaynak, O., “Neuro-fuzzy Control of Antilockbraking System Using Sliding Mode Incremental Learning Algorithm,” Neuro-computing, Vol. 74, 2011, pp. 1883–1893.
[13] Farooq, U., Khan, M.S.,  Ahmed, K., Saeed, M.A. and Abbas, S., “Autonomous System Controller for Vehicles Using Neuro-Fuzzy, International Journal of Scientific & Engineering Research, Vol. 2 No. 6, 2011.
[14] Roy, S.S., “Design of Adaptive Neuro-Fuzzy Inference System for Predicting Surface Roughness in Turning Operation,” Journal of Scientific and Industrial Research, Vol. 64, 2005. pp. 1087–1094.
[15] Kim, T.W., Yuh, J., “Fast On-line Neuro-Fuzzy Controller for Autonomous Under-Water Vehicles,” International Society of Offshore and Polar Engineers, Seoul, Korea, 2005.
[16] Li, Y. and Liu, Y., Real-Time Tip-Over Prevention And Path Following Control Forredundant Nonholonomic Mobile Modular Manipulators Via Fuzzy And Neural-Fuzzy Approaches, Journal of Dynamic Systems, Measurement, and Control,Vol.128, 2006, pp. 753–764.
[17] Kermani, M. Z. and Teshnehlab, M., Using Adaptive Neuro-Fuzzy Inference System For Hydrological Time Series Prediction, Applied Soft Computing, Vol. 8, 2008, pp.928-936.
[18] Kurnaz, S., Cetin, O. and Kaynak, O., Adaptive Neuro-Fuzzy Inference System Basedautonomous Flight Control of Unmanned Air Vehicles, Expert Systems with Applications, Vol. 37, 2010, pp. 1229–1234.
[19] Pérez, J., Gajate, A., Milanés, V., Onieva, E. and Santos, M., Design And Implementation of A Neuro-Fuzzy System For Longitudinal Control of Autonomous Vehicles, IEEE International Conference on Fuzzy Systems, 2010, pp. 1–6.
[20] Oroumieh, A., Malaek, M. A., Ashrafizadeh, M., S. M. B., “Aircraft Design Cycle Time Reduction Using Artificial Intelligence,” Aerospace Science and Technology, Vol. 26 No. 1, April- May 2013, pp. 244-258.
[21] Terrel, K. and Zein-Sabatto, S., “Intelligent Reconfigurable Control System for Aircraft Flight Control,” IEEE Southeast Conference, 2017, pp. 1-7.
[22] Malaek, S. M. B., Sadati, N., Izadi, H. and Pakmehr, M., “Intelligent Autolanding Controller Design using Neural Networks and Fuzzy Logic,” IEEE Conference, Vol.1, 2004, pp. 365-373.
[23]Available, [on line]: https://eoportal.org/ web/eoportal/satellite-missions, accessed October 15, 2017.
[24] Jantzen, J., Neurofuzzy Modelling, Technical University of Denmark, 2005.
[25] Wolkenhauer, O., Fuzzy Mathematics in Systems Theory and Data Analysis, John Wiley & Sons Inc., 2001.
[26] Shing, J. and Jang, R., ANFIS: Adaptive- Network-Based Fuzzy Inference System, IEEE Transaction on Systems, Man and Cybernetics, Vol. 23, No. 3, May/June 1993.
[27] Walia, N., Singh, H. and Sharma, A., “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey,” International Journal of Computer Applications, Vol. 123, No. 13, 2015.
[28] Kaur, R., Lal Sangal, A. and Kumar, K., “Modeling and Simulation of Adaptive Neuro-fuzzy Based Intelligent System For Predictive Stabilization in Structured Overlay Networks,” International Journal of Engineering Science and Technology, Vol. 20, Issue 1, 2017, pp. 310-320.