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

Authors:
Liguo Weng;Wenchuan Cai;M. J. Zhang;X. H. Liao;David Y. Song
Affiliations:
Center for Cooperative Systems, Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 East Market St. Greensboro, NC, 27411, USA;Center for Cooperative Systems, Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 East Market St. Greensboro, NC, 27411, USA;Center for Cooperative Systems, Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 East Market St. Greensboro, NC, 27411, USA;Center for Cooperative Systems, Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 East Market St. Greensboro, NC, 27411, USA;Center for Cooperative Systems, Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 East Market St. Greensboro, NC, 27411, USA
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 5
Cited 0

Improving signal prediction performance of neural networks through multiresolution learning approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A stable neural network-based observer with application to flexible-joint manipulators

IEEE Transactions on Neural Networks
Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

IEEE Transactions on Neural Networks
A Convolutional Neural Network Approach for Objective Video Quality Assessment

IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.