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

Open access

Abstract

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller.

[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.

[2] W. Shyy, Y. Lian, J. Tang, D. Viieru, and H. Liu, Aerodynamics of low Reynolds number flyers. Cambridge University Press, 2007, vol. 22.

[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.

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

[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.

[6] S. P. Sane, The aerodynamics of insect flight, Journal of experimental biology, vol. 206, no. 23, pp. 4191–4208, 2003.

[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.

[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.

[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] 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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

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

[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.

[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.

[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.

[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.

[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.

[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.

[33] Z. J. Wang, The role of drag in insect hovering, Journal of Experimental Biology, vol. 207, no. 23, pp. 4147–4155, 2004.

[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.

[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.

[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.

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

Journal Information

CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813

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