Abstract
This paper addresses the problem of wing motion control of flapping wing Micro Air Vehicles (MAVs). Inspired by hummingbird’s wing structure as well as the construction of its skeletal and muscular components, a dynamic model for flapping wing is developed. As the model is highly nonlinear and coupled with unmeasurable disturbances and uncertainties, traditional strategies are not applicable for flapping wing motion control. A new approach called neural-memory based control is proposed in this work. It is shown that this method is able to learn from past control experience and current/past system behavior to improve its performance during system operation. Furthermore, much less information about the system dynamics is needed in construction such a control scheme as compared with traditional NN based methods. Both theoretical analysis and computer simulation verify its effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Grasmeyer, J.M., Keennon, M.T.: Development of the Black Widow Micro Air Vehicle. AIAA Paper, 2001-0127 (2000)
Morris, S., Holden, M.: Design of Micro Air Vehicles and Flight Test Validation. In: Proceedings of the Conference on Fixed, Flapping and Rotary Wing Vehicles at Very Low Reynolds Numbers, Dept. of Aerospace and Mechanical Engineering, Notre Dame Univ., Notre Dame, Indiana (2000)
Pornsin-Sisirak, T.N., Lee, S.W., Nassef, H., Grasmeyer, J., Tai, Y.C., Ho, C.M., Keenon, M.: MEMS Wing Technology for a Battery-Powered Ornithopter. In: Proceedings of the 13th Annual IEEE International Conference on Micro Electro Mechanical Systems, Miyazaki, Japan, pp. 799–804 (2000)
Dickinson, M.H., Lehmann, F., Sane, S.P.: Wing Rotation and the Aerodynamic Basis of Insect Flight. Science 284, 1954–1960 (1999)
Karayiannis, N.B., Xiong, Y.: Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification. IEEE Transactions on Neural Networks 17, 1222–1234 (2006)
Yao, L., Xu, L.: Improving Signal Prediction Performance of Neural Networks Through Multiresolution Learning Approach. IEEE Transactions on Systems, Man and Cybernetics 36, 341–352 (2006)
Le Callet, P., Viard-Gaudin, C., Barba, D.: Convolutional Neural Network Approach for Objective Video Quality Assessment. IEEE Transactions on Neural Networks 17, 1316–1327 (2006)
Liu, S., Wang, J.: A Simplified Dual Neural Network for Quadratic Programming with Its KWTA Application. IEEE Transactions on Neural Network 17, 1500–1510 (2006)
Abdollahi, F., Talebi, H.A., Patel, R.V.: A Stable Neural Network-Based Observer With Application to Flexible-Joint Manipulators. IEEE Transactions on Neural Networks 17, 118–129 (2006)
David, L.R., Eric, C.S.: Mechanization and Control Concepts for Biologically Inspired Micro Air Vehicles. Journal of Aircraft 41, 1257–1265 (2004)
Grant, D., Abdulrahim, M., Lind, R.: Flight Dynamics of a Morphing Aircraft Utilizing Independent Multiple-Joint Wing Sweep. In: AIAA Atmospheric Flight Mechanics Conference and Exhibit, AIAA-2006-6505 (2006)
Sibilski, K., Loroch, L., Buler, W., Zyluk, A.: Modeling and Simulation of the Nonlinear Dynamic Behavior of a Flapping Wings Micro-Aerial-Vehicle. In: 42nd AIAA Aerospace Sciences Meeting and Exhibit, AIAA-2004-541 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weng, L., Cai, W., Zhang, M.J., Liao, X.H., Song, D.Y. (2007). Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-72383-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
eBook Packages: Computer ScienceComputer Science (R0)