Development of C-Means Clustering Based Adaptive Fuzzy Controller for a Flapping Wing Micro Air Vehicle

[1] C. P. Ellington, The novel aerodynamics of insect flight: applications to micro-air vehicles, Journal of Experimental Biology, vol. 202, no. 23, pp. 3439–3448, 1999.10.1242/jeb.202.23.3439Search in Google Scholar

[2] W. Shyy, Y. Lian, J. Tang, D. Viieru, and H. Liu, Aerodynamics of low Reynolds number flyers. Cambridge University Press, 2007, vol. 22.10.1017/CBO9780511551154Search in Google Scholar

[3] W. Shyy, H. Aono, S. K. Chimakurthi, P. Trizila, C.-K. Kang, C. E. Cesnik, and H. Liu, Recent progress in flapping wing aerodynamics and aeroelasticity,” Progress in Aerospace Sciences, vol. 46, no. 7, pp. 284–327, 2010.10.1016/j.paerosci.2010.01.001Search in Google Scholar

[4] H. Tennekes, The simple science of flight: from insects to jumbo jets. MIT press, 2009.Search in Google Scholar

[5] A. P. Willmott and C. P. Ellington, The mechanics of flight in the hawkmoth manduca sexta. i. kinematics of hovering and forward flight. Journal of Experimental Biology, vol. 200, no. 21, pp. 2705–2722, 1997.10.1242/jeb.200.21.2705Search in Google Scholar

[6] S. P. Sane, The aerodynamics of insect flight, Journal of experimental biology, vol. 206, no. 23, pp. 4191–4208, 2003.10.1242/jeb.00663Search in Google Scholar

[7] Y. Lin, Y. Xu, H. Chen, M. J. Bender, A. L. Abbott, and R. Müller, Optimal Threshold and LoG Based Feature Identification and Tracking of Bat Flapping Flight, in Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017, pp. 418–426.10.1109/WACV.2017.53Search in Google Scholar

[8] S. M. Nogar, A. Gogulapati, J. J. McNamara, A. Serrani, M. W. Oppenheimer, and D. B. Doman, Control-Oriented Modeling of Coupled Electromechanical-Aeroelastic Dynamics for Flapping-Wing Vehicles,” Journal of Guidance, Control, and Dynamics, 2017.10.2514/1.G002503Search in Google Scholar

[9] J. Zhang and X. Deng, Resonance principle for the design of flapping wing micro air vehicles, IEEE Transactions on Robotics, vol. 33, no. 1, pp. 183–197, 2017.10.1109/TRO.2016.2626457Search in Google Scholar

[10] C. Zhang and C. Rossi, A review of compliant transmission mechanisms for bio-inspired flapping-wing micro air vehicles, Bioinspiration & biomimetics, vol. 12, no. 2, p. 025005, 2017.10.1088/1748-3190/aa58d3Search in Google Scholar

[11] M. W. Oppenheimer, D. O. Sigthorsson, I. E. Weintraub, and D. B. Doman, Wing Design and Testing for a Tailless Flapping Wing Micro Air Vehicle, in AIAA Guidance, Navigation, and Control Conference, 2017, p. 1271.10.2514/6.2017-1271Search in Google Scholar

[12] M. S. Couceiro, N. Ferreira, and J. Machado, Modeling and control of a dragonfly-like robot, Journal of Control Science and Engineering, vol. 2010, p. 5, 2010.10.1155/2010/643045Search in Google Scholar

[13] J. Sun, C. Pan, J. Tong, and J. Zhang, Coupled model analysis of the structure and nano-mechanical properties of dragonfly wings, IET nanobiotechnology, vol. 4, no. 1, pp. 10–18, 2010.10.1049/iet-nbt.2009.000920170254Search in Google Scholar

[14] M. Okamoto, K. Yasuda, and A. Azuma, Aerodynamic characteristics of the wings and body of a dragonfly, Journal of Experimental Biology, vol. 199, no. 2, pp. 281–294, 1996.10.1242/jeb.199.2.2819317808Search in Google Scholar

[15] S. Sudo, K. Tsuyuki, T. Ikohagi, F. Ohta, S. Shida, and J. Tani, A study on the wing structure and flapping behavior of a dragonfly, JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, vol. 42, no. 3, pp. 721–729, 1999.10.1299/jsmec.42.721Search in Google Scholar

[16] J. S. Jang and C. Tomlin, Longitudinal stability augmentation system design for the DragonFly UAV using a single GPS receiver, in AIAA Guidance, Navigation, and Control Conference, AIAA, vol. 5592, 2003.10.2514/6.2003-5592Search in Google Scholar

[17] J. M. Kok and J. Chahl, Design and manufacture of a self-learning flapping wing-actuation system for a dragonfly-inspired MAV, in 54th AIAA Aerospace Sciences Meeting, 2016, p. 1744.10.2514/6.2016-1744Search in Google Scholar

[18] Q.-V. Nguyen, W. L. Chan, and M. Debiasi, Design, fabrication, and performance test of a hovering-based flapping-wing micro air vehicle capable of sustained and controlled flight, 2014.Search in Google Scholar

[19] C.-p. Du, J.-x. Xu, and Y. Zheng, Application of iterative learning tuning to a dragonfly-like flapping wing micro aerial vehicle, in Control and Decision Conference (CCDC), 2016 Chinese. IEEE, 2016, pp. 4215–4220.Search in Google Scholar

[20] M. M. Ferdaus, S. G. Anavatti, M. Pratama, and M. A. Garratt, Online Identification of a Rotary Wing Unmanned Aerial Vehicle from Data Streams,” 2017.Search in Google Scholar

[21] S. B. Hu, W. H. Lu, Z. Y. Chen, L. Lei, and Y. X. Zhang, Attitude control of flapping wing micro aerial vehicle based on double fuzzy sliding mode control, in Advanced Materials Research, vol. 468. Trans Tech Publ, 2012, pp. 704–707.10.4028/www.scientific.net/AMR.468-471.704Search in Google Scholar

[22] A. A. Al-Mahasneh, S. G. Anavatti, and M. Garratt, Nonlinear Multi-Input Multi-Output System Identification using Neuro-Evolutionary Methods for a Quadcopter, IEEE, pp. 217–222, 2017.10.1109/ICACI.2017.7974512Search in Google Scholar

[23] M. M. Ferdaus, S. G. Anavatti, M. A. Garratt, and M. Pratama, Fuzzy Clustering based Nonlinear System Identification and Controller Development of Pixhawk based Quadcopter, in Advanced Computational Intelligence (ICACI), 2017 IEEE International Conference on. IEEE, 2017, pp. 223–230.10.1109/ICACI.2017.7974513Search in Google Scholar

[24] M. M. Ferdaus, S. G. Anavatti, M. A. Garratt, and M. Pratama, Fuzzy Clustering based Modelling and Adaptive Controlling of a Flapping Wing Micro Air Vehicle, in Computational Intelligence (IEEE SSCI), 2017 IEEE Symposium Series on. IEEE, 2017, pp. 1914–1919.10.1109/SSCI.2017.8280969Search in Google Scholar

[25] M. M. Ferdaus, M. Pratama, S. G. Anavatti, and M. A. Garratt, Evolving Neuro-Fuzzy System based Online Identification of a Bio-inspired Flapping Wing Micro Aerial Vehicle, in Computational Intelligence (IEEE SSCI), 2017 IEEE Symposium Series on. IEEE, 2017, pp. 2840–2847.10.1109/SSCI.2017.8285226Search in Google Scholar

[26] C. Zhang, Design and control of flapping wing micro air vehicles, 2016. [Online]. Available: http://oa.upm.es/44319/Search in Google Scholar

[27] M. Ferdaus, M. Pratama, S. G. Anavatti, M. A. Garratt, and Y. Pan, Generic evolving self-organizing neuro-fuzzy control of bio-inspired unmanned aerial vehicles, arXiv preprint arXiv:1802.00635, 2018.Search in Google Scholar

[28] A. J. Al-Mahasneh, S. Anavatti, and M. Garratt, Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks, in Computational Intelligence (IEEE SSCI), 2017 IEEE Symposium Series on. IEEE, 2017.10.1109/SSCI.2017.8280951Search in Google Scholar

[29] T. S. Clawson, S. Ferrari, S. B. Fuller, and R. J. Wood, Spiking neural network (SNN) control of a flapping insect-scale robot, in Decision and Control (CDC), 2016 IEEE 55th Conference on. IEEE, 2016, pp. 3381–3388.10.1109/CDC.2016.7798778Search in Google Scholar

[30] L. Weng, M. Xia, K. Hu, and A. Wang, Micro Aerial Vehicle (MAV) Flapping Motion Control Using an Immune Network with Different Immune Factors,” International Journal of Advanced Robotic Systems, vol. 10, no. 8, p. 305, 2013.10.5772/56733Search in Google Scholar

[31] M. S. Couceiro, N. M. Ferreira, and J. T. Machado, Hybrid adaptive control of a dragonfly model, Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 2, pp. 893–903, 2012.10.1016/j.cnsns.2011.05.031Search in Google Scholar

[32] J. Kok and J. Chahl, A low-cost simulation platform for flapping wing MAVs, in SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring. International Society for Optics and Photonics, 2015, pp. 94 290L–94 290L.10.1117/12.2084142Search in Google Scholar

[33] Z. J. Wang, The role of drag in insect hovering, Journal of Experimental Biology, vol. 207, no. 23, pp. 4147–4155, 2004.10.1242/jeb.01239Search in Google Scholar

[34] J. C. Bezdek, A convergence theorem for the fuzzy isodata clustering algorithms, IEEE transactions on pattern analysis and machine intelligence, no. 1, pp. 1–8, 1980.10.1109/TPAMI.1980.4766964Search in Google Scholar

[35] M. M. Ferdaus, M. Pratama, S. G. Anavatti, and M. A. Garratt, A generic self-evolving neuro-fuzzy controller based high-performance hexacopter altitude control system, arXiv preprint arXiv:1805.02508, 2018.10.1109/SMC.2018.00475Search in Google Scholar

[36] M. M. Ferdaus, S. G. Anavatti, M. A. Garratt, and M. Pratama, Development of a sliding mode control based adaptive fuzzy controller for a flapping flight, arXiv preprint arXiv:1806.02945, 2018.10.2478/jaiscr-2018-0027Search in Google Scholar