Online parameter estimation of a miniature unmanned helicopter using neural network techniques

DSpace/Manakin Repository

Show simple item record

dc.contributor.author Kantue, Paulin
dc.date.accessioned 2012-02-01T14:02:48Z
dc.date.available 2012-02-01T14:02:48Z
dc.date.issued 2012-02-01
dc.identifier.uri http://hdl.handle.net/10539/11235
dc.description.abstract The online aerodynamic parameter estimation of a miniature unmanned helicopter using Neural Network techniques has been presented. The simulation model for the miniature helicopter was developed using the MATLAB/ SIMULINK software tool. Three trim conditions were analyzed: hover flight, 10m/s forward flight and 20m/s forward flight. Radial Basis Function (RBF) online learning was achieved using a moving window algorithm which generated an input-output data set at each time step. RBF network online identification was achieved with good robustness to noise for all flight conditions. However, the presence of atmospheric turbulence and sensor noise had an adverse effect on network size and memory usage. The Delta Method (DM) and the Modified Delta Method (MDM) was investigated for the NN-based online estimation of aerodynamic parameters. An increasing number high confidence estimated parameters could be extracted using the MDM as the helicopter transitioned from hover to forward flight. en_US
dc.language.iso en en_US
dc.title Online parameter estimation of a miniature unmanned helicopter using neural network techniques en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search WIReDSpace


Advanced Search

Browse

My Account

Statistics