Compensation Control for Grasped Object of Dual-arm Space Robot with Closed-chain Based on Radial Basis Function Neural Network

2011 ◽  
Vol 47 (07) ◽  
pp. 38 ◽  
Author(s):  
Zhihuang CHEN
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Space Robot ◽  
Compensation Control ◽  
Closed Chain ◽  
Radial Basis ◽  
Dual Arm

scholarly journals Adaptive Control for Dual-Arm Robotic System Based on Radial Basis Function Neural Network 

2020 ◽  
Vol 31 (1) ◽  
pp. 50-59
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Robotic System ◽  
Object A ◽  
Radial Basis ◽  
Stability Principle ◽  
Asymptotical Convergence ◽  
Dual Arm

The paper has developed an adaptive control using neural network for controlling a dual-arm robotic system in moving a rectangle object to the desired trajectories. Firstly, the overall dynamics of the manipulators and the object have been derived based on Euler-Lagrangian principle. And then based on the dynamics, a controller has been proposed to achieve the desired trajectories of the grasping object. A radial basis function neural network has been applied to compensate uncertainties of dynamic parameters. The adaptive algorithm has been derived owning to the Lyapunov stability principle to guarantee asymptotical convergence of the closed dynamic system. Finally, simulation work on MatLab has been carried out to reconfirm the accuracy and the effectiveness of the proposed controller.

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