Please use this identifier to cite or link to this item:
Title: Approximation-based control of uncertain helicopter dynamics
Authors: Ge, S.S. 
Ren, B. 
Tee, K.P.
Lee, T.H. 
Issue Date: 2009
Citation: Ge, S.S., Ren, B., Tee, K.P., Lee, T.H. (2009). Approximation-based control of uncertain helicopter dynamics. IET Control Theory and Applications 3 (7) : 941-956. ScholarBank@NUS Repository.
Abstract: In this study, the altitude and yaw angle tracking is considered for a scale model helicopter, mounted on an experimental platform, in the presence of model uncertainties, which may be caused by unmodelled dynamics, or aerodynamical disturbances from the environment. To deal with the uncertainties, approximation-based techniques using neural network (NN) are proposed. In particular, two different types of NN, namely multilayer neural network and radial basis function neural network are adopted in control design and stability analysis. Based on Lyapunov synthesis, the proposed adaptive NN control ensures that both the altitude and the yaw angle track the given bounded reference signals to a small neighbourhood of zero, and guarantees semiglobal uniform ultimate boundedness of all the closed-loop signals at the same time. The effectiveness of the proposed control is illustrated through extensive simulations. Compared with the model-based control, approximation-based control yields better tracking performance in the presence of model uncertainties. © The Institution of Engineering and Technology 2009.
Source Title: IET Control Theory and Applications
ISSN: 17518644
DOI: 10.1049/iet-cta.2008.0103
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jul 30, 2021


checked on Jul 30, 2021

Page view(s)

checked on Aug 3, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.