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Biomimetic-based output feedback for attitude stabilization of a flapping-wing micro aerial vehicle

Published online by Cambridge University Press:  10 April 2013

H. Rifaï ,
J.-F. Guerrero-Castellanos ,
N. Marchand  and
G. Poulin-Vittrant
H. Rifaï*
Affiliation:
LISSI, 120-122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France
J.-F. Guerrero-Castellanos
Affiliation:
Faculty of Electronics, Autonomous University of Puebla (BUAP), Puebla, Mexico
N. Marchand
Affiliation:
GIPSA Lab, Control Systems Department, ENSE3/CNRS, Saint Martin d'Hères, France
G. Poulin-Vittrant
Affiliation:
Greman, UMR 7347 CNRS, University François Rabelais de Tours, Site de Blois, Rue de la Chocolaterie, 41000 Blois, France
*
*Corresponding author. E-mail: hala.rifai@u-pec.fr
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Summary

The paper deals with the development of a bounded control law for Flapping-wing Micro Aerial Vehicles that mimics a strategy adopted by animal flapping flyers to stabilize their orientation. The control consists on generating torques about the body's principal axes by means of a modulation of the wing angle amplitudes. It is known that flapping flyers orient their body without any numerical computation or estimation of their current attitude. Therefore, the proposed control law is computed using the direct measurements of onboard sensors mimicking animal sensitive organs, more specifically the halteres, legs sensilla, and magnetic sense. The technological equivalents of these biological sensors are three rate gyros, a tri-axis accelerometer, and a tri-axis magnetometer, respectively. Besides, the control signal is bounded to keep the wing angle amplitudes below the maximal values. Owing to its simplicity, this control law is suitable for applications where onboard computational resources are limited. The stability of the closed-loop system is proved based on Lyapunov analysis and averaging theory. The effectiveness of the proposed control law is shown in simulations. The robustness with respect to external disturbances is also shown emphasizing the importance and need of the bounded control.

Keywords

Aerial roboticsBiomimetic robotsControl of robotic systemsBounded controlMicro robotics
Type
Articles
Information
Robotica , Volume 31 , Issue 6 , September 2013 , pp. 955 - 968
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Alexander, D. E., “Wind tunnel studies of turns by flying dragonflies,” J. Exp. Biol. 122, 81–98 (1986).CrossRefGoogle ScholarPubMed
2.Alexander, D. E. and Vogel, S., Nature's Flyers: Birds, Insects and the Biomechanics of Flight (Johns Hopkins University Press, Baltimore, 2004).CrossRefGoogle Scholar
3.Bullo, F., “Averaging and vibrational control of mechanical systems,” SIAM J. Control Optim. 41 (2), 452–562 (2002).CrossRefGoogle Scholar
4.Campolo, D., Barbera, G., Schenato, L., Pi, L., Deng, X. and Guglielmelli, E., “Attitude stabilization of a biologically inspired robotic housefly via dynamic multimodal attitude estimation,” Adv. Robot. 23 (7–8), 955–977 (2009).CrossRefGoogle Scholar
5.Campolo, D., Sitti, M. and Fearing, R. S., “Efficient charge recovery method for driving piezoelectric actuators with quasi-square waves,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 50 (3), 237–244 (2003).CrossRefGoogle ScholarPubMed
6.Chapman, R., “The Insects: Structure and Function, 4th ed. (Cambridge University Press, Cambridge, 1998).CrossRefGoogle Scholar
7.Epstein, M., Waydo, S., Fuller, S.-B., Dickson, W., Straw, A., Dickinson, M.-H., and Murray, R.-M.. Biologically inspired feedback design for drosophila flight. In American Control Conference, New York, USA (2007) pp. 3395–3401.Google Scholar
8.Cheinet, P., Canuel, B., Santos, F. Pareira Dos, Gauguet, A., Leduc, F. and Landragin, A., “Measurement of the sensitivity function in time-domain atomic interferometer,” IEEE Trans. Instrum. Meas. 57 (6), 1141–1148 (2008).CrossRefGoogle Scholar
9.Chung, S.-J. and Dorothy, M., “Neurobiologically inspired control of engineered flapping flight,” J. Guid. Control Dyn. 33 (2), 440–453 (2010).CrossRefGoogle Scholar
10.Couceiro, M.-S., Luz, J.-M., Figueiredo, C.-M. and Fonseca, N.-M. Ferreira, “Modeling and control of biologically inspired flying robots,” Robotica 30 (1), 107–121 (2012).CrossRefGoogle Scholar
11.Deng, X., Schenato, L. and Sastry, S., “Model Identification and Attitude Control for a Micromechanical Flying Insect Including Thorax and Sensor Models,” Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003) pp. 1152–1157.Google Scholar
12.Deng, X., Schenato, L., Wu, W.-C. and Sastry, S., “Flapping flight for biomimetic robotic insects: Part I system modeling,” IEEE Trans. Robotics 22 (4), 776–788 (2006a).CrossRefGoogle Scholar
13.Shyy, W., Aono, H., Chimakurthi, S.-K., Trizila, P., Kang, C.-K., Cesnik, C.E. and Liu, H.. Recent progress in flapping wing aerodynamics and aeroelasticity. Progress in Aerospace Sciences, 46 (7), 284–327 (2010).CrossRefGoogle Scholar
14.Deng, X., Schenato, L., Wu, W.-C. and Sastry, S., “Flapping flight for biomimetic robotic insects: Part II flight control design,” IEEE Trans. Robotics 22 (4), 789–803 (2006b).CrossRefGoogle Scholar
15.Dickinson, M., Lehmann, F.-O. and Sane, S., “Wing rotation and the aerodynamic basis of insect flight,” Science 284 (5422), 1954–1960 (1999).CrossRefGoogle ScholarPubMed
16.Dudley, R., The Biomechanics of Insect Flight: Form, Function, Evolution (Princeton Univesity Press, Princeton, 2002).Google Scholar
17.Duhamel, P.-E., Pérez-Arancibia, N.-O., Barrows, G.-L. and Wood, R.-J., “Altitude Feedback Control of a Flapping-Wing Microrobot Using an On-Board Biologically Inspired Optical Flow Sensor,” Proceedings of the IEEE International Conference on Robotics and Automation, St Paul, MN, USA (2012) pp. 4228–4235.Google Scholar
18.Finio, B.-M., Eum, B., Oland, C. and Wood, R.-J., “Asymmetric Flapping for a Robotic Fly Using a Hybrid Power-Control Actuator,” Proceedings of the International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009) pp. 2755–2762.Google Scholar
19.Finio, B.-M., Pérez-Arancibia, N.-O. and Wood, R.-J., “System Identification and Linear Time-Invariant Modeling of an Insect-Sized Flapping-Wing Micro Air Vehicle,” Proceedings of the International Conference on Intelligent Robots and Systems, San Francisco, CA, USA (2011) pp. 1107–1114.Google Scholar
20.Hedrick, T. and Daniel, T., “Flight control in the hawkmoth Manduca sexta: the inverse problem of hovering,” J. Exp. Biol. 209 (16), 3114–3130 (2006).CrossRefGoogle ScholarPubMed
21.Janocha, H. and Stiebel, C., “New Approach to a Switching Amplifier for Piezoelectric Actuators,” Proceedings of the 6th International Conference on New Actuators, Bremen, Germany (1998) pp. 189–192.Google Scholar
22.Khalil, H., Nonlinear Systems (Prentice Hall, Upper Saddle River, NJ, 2002).Google Scholar
23.Kuhnen, K., Janocha, H., Thull, D. and Kugi, A., “A New Drive Concept for High-Speed Positioning of Piezoelectric Actuators,” Proceedings of the 10th International Conference on New Actuators, Bremen, Germany (2006) pp. 82–85.Google Scholar
24.Lentink, D. and Biewener, A.-A., “Nature-inspired flight –beyond the leap,” Bioinspiration and Biomimetics 5 (4), 040201 (2010).CrossRefGoogle ScholarPubMed
25.Markley, F. L., Crassidis, J. L. and Cheng, Y., “Nonlinear Attitude Filtering Methods,” AIAA Guidance, Navigation, and Control Conference, San Fancisco, California (2005) paper no. 5927.Google Scholar
26.Mountcastle, A.-M. and Danie, T.-L., “Vortexlet models of flapping flexible wings show tuning for force production and control,” Bioinspiration and Biomimetics 5 (4), 045005 (2010).CrossRefGoogle ScholarPubMed
27.Nachtigall, W. and Wilson, D.-M., “Neuro-muscular control of dipteran flight,” J. Exp. Biol. 47, 77–97 (1967).CrossRefGoogle ScholarPubMed
28.Oppenheimer, M.-W., Doman, D.-B. and Sightorsson, D.-O.,“Dynamics and Control of a Minimally Actuated Biomimetic Vehicle: Part II Control,” Proceedings of the AIAA Guidance, Navigation and Control Conference, Chicago, Illinois, USA (2009) p. 6161.Google Scholar
29.Oppenheimer, M.-W., Doman, D.-B. and Sightorsson, D.-O., “Dynamics and Control of a Biomimetic Vehicle Using Biased Wingbeat Forcing Functions: Part I Aerodynamic Model,” Proceedings of the 48th AIAA Aerospace Sciences Meeting, Orlando, Florida, USA (2010) p. 1023.Google Scholar
30.Perez-Arancibia, N.-O., Ma, K.-Y., Galloway, K.-C., Greenberg, J.-D. and Wood, R.-J., “First controlled vertical flight of a biologically inspired microrobot,” Bioinspiration and Biomimetics 6 (3), 036009 (2011).CrossRefGoogle ScholarPubMed
31.Rakotomamonjy, T., Ouladsine, M. and LeMoing, T., “Longitudinal modelling and control of a flapping-wing micro aerial vehicle,” Control Engineering Practice 18 (7), 679–690 (2010).CrossRefGoogle Scholar
32.Renaudin, A., Zhang, V., Tabourier, P., Camart, J. and Druon, C., “Droplet Manipulation Using SAW Actuation for Integrated Microfluidics,” μTAS, Malmö, Sweden (2004) pp. 551–553.Google Scholar
33.Rifai, H., Marchand, N. and Poulin, G., “Bounded Control of a Flapping Wing Micro Drone in Three Dimensions,” Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, California, USA (2008) pp. 164–169.CrossRefGoogle Scholar
34.Sane, S., “Review the aerodynamics of insect flight,” J. Exp. Biol. 206 (23), 4191–4208 (2003).CrossRefGoogle ScholarPubMed
35.Schenato, L., Campolo, D. and Sastry, S., “Controllability Issues in Flapping Flight for Biomimetic Micro Aerial Vehicles (MAVs),” International Conference on Decision and Control Maui, Hawaii, USA (2003) pp. 6441–6447.Google Scholar
36.Schenato, L., Wu, W.-C. and Sastry, S., “Attitude control for a micromechanical flying insect via sensor output feedback,” IEEE Trans. Robot. Autom. 20 (1), 93–106 (2004).CrossRefGoogle Scholar
37.Senda, K., Sawamoto, M., Kitamura, M. and Obara, T., “Effects of Flexibly Torsional Wings in Flapping-of-Wings Flight of Butterfly,” World Automation Congress, Hawaii, USA (2008) pp. 1–6.Google Scholar
38.Sreetharan, P.-S. and Wood, R.-J., “Passive torque regulation in an underactuated flapping wing robotic insect,” Auton. Robots 31 (2–3), 225–234 (2011).CrossRefGoogle Scholar
39.Steltz, E. and Fearing, R.-S., “Dynamometer Power Output Measurements of Piezoelectric Actuators,” Proceedings of the International Conference on Intelligent Robots and Systems, San Diego, California, USA (2007) pp. 3980–3986.Google Scholar
40.Thomson, S.-L., Mattson, C.-A., Colton, M.-B., Harston, S.-P., Carlson, D.-C. and Cutler, M., “Experiment-Based Optimization of Flapping Wing Kinematics,” Proceedings of the 47th AIAA Aerospace Sciences Meeting, Orlando, Florida (2009) p. 874.Google Scholar
41.Vela, P. A., Averaging and Control of Nonlinear Systems, Ph.D. Thesis, (California: California Institute of Technology, 2003).Google Scholar