Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings

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.

[1]  T.N. Pornsin-Sirirak,et al.  MEMS wing technology for a battery-powered ornithopter , 2000, Proceedings IEEE Thirteenth Annual International Conference on Micro Electro Mechanical Systems (Cat. No.00CH36308).

[2]  Yaohua Xiong,et al.  Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification , 2006, IEEE Transactions on Neural Networks.

[3]  Matthew T. Keennon,et al.  Development of the Black Widow Micro Air Vehicle , 2001 .

[4]  David L. Raney,et al.  Mechanization and Control Concepts for Biologically Inspired Micro Air Vehicles , 2004 .

[5]  Heidar Ali Talebi,et al.  A stable neural network-based observer with application to flexible-joint manipulators , 2006, IEEE Transactions on Neural Networks.

[6]  M. Dickinson,et al.  Wing rotation and the aerodynamic basis of insect flight. , 1999, Science.

[7]  Shubao Liu,et al.  A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application , 2006, IEEE Transactions on Neural Networks.

[8]  Mujahid Abdulrahim,et al.  [american institute of aeronautics and astronautics aiaa atmospheric flight mechanics conference and exhibit - keystone, colorado ()] aiaa atmospheric flight mechanics conference and exhibit - flight dynamics of a morphing aircraft utilizing independent multiple-joint wing sweep , 2006 .

[9]  Yao Liang,et al.  Improving Signal Prediction Performance of Neural Networks Through Multiresolution Learning Approach , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Krzysztof Sibilski,et al.  MODELING AND SIMULAT ION OF THE NONLINEAR DYNAMIC BEHAVIOR OF A FLAPPING WINGS MICRO - AERIAL - VEHI CLE , 2004 .

[11]  Christian Viard-Gaudin,et al.  A Convolutional Neural Network Approach for Objective Video Quality Assessment , 2006, IEEE Transactions on Neural Networks.