scholarly journals Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Congqing Wang ◽  
Linfeng Wu

The dynamic model of a planar free-floating flexible redundant space manipulator with three joints is derived by the assumed modes method, Lagrange principle, and momentum conservation. According to minimal joint torque’s optimization (MJTO), the state equations of the dynamic model for the free-floating redundant space manipulator are described. The PD control using the tracking position error and velocity error in the manipulator is introduced. Then, the chaotic dynamic behavior of the manipulator is analyzed by chaotic numerical methods, in which time series, phase plane portrait, Poincaré map, and Lyapunov exponents are used to analyze the chaotic behavior of the manipulator. Under certain conditions for the joint torque optimization and initial values, chaotic vibration motion of the space manipulator can be observed. The chaotic time series prediction scheme for the space manipulator is presented based on the theory of phase space reconstruction under Takens’ embedding theorem. The trajectories of phase space can be reconstructed in embedding space, which are equivalent to the original space manipulator in dynamics. The one-step prediction model for the chaotic time series and the chaotic vibration was established by using support vector regression (SVR) prediction model with RBF kernel function. It has been proved that the SVR prediction model has a good performance of prediction. The experimental results show the effectiveness of the presented method.

2011 ◽  
Vol 255-260 ◽  
pp. 3594-3599 ◽  
Author(s):  
Guo Rong Yu ◽  
Ju Rui Yang ◽  
Zi Qiang Xia

Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series. Applying the Chaos and non-linear time series based on the support vector machine regression principle, this paper proposes a method and its characteristic and the choosing of key parameters to forecast and set up models. According to Phase Space Reconstruction theory carry on reconstruction of Phase Space to monthly surface flow course, have discussed that probed into the non-linear prediction model of time series of Chaos of the support vector machine, application in the monthly surface flow, have introduce it through to the nuclear function of the base in the course of setting up the model of support vector machine, has simplified the course of solving the non-linear problems. The instance indicates that the model can deal with the complicated hydrology data array well, and there is the good prediction precision.


1998 ◽  
Vol 58 (2) ◽  
pp. 2640-2643 ◽  
Author(s):  
A. K. Alparslan ◽  
M. Sayar ◽  
A. R. Atilgan

Sign in / Sign up

Export Citation Format

Share Document