A Dual Neural Network Applied to Drift-Free Resolution of Five-Link Planar Robot Arm
In this paper,a recurrent neural network (termed, dual neural network)is revisited and applied to the online joint angle drift-free redundancy-resolution of a five-link planar robot manipulator.To do this,a drift-free criterion is exploited in the form of a quadratic function.In addition,the drift-free scheme could incorporate multiple joint physical limits such as joint limits and joint velocity limits simultaneously.Such a scheme is finally reformulated as a quadratic-programming (QP)problem.Similar to other new types of recurrent neural networks,the dual neural network is piecewise-linear as well and has a simple architecture of only one layer of neurons.As a QP real-time solver,the dual neural network could globally exponentially converge to the optimal solution of a strictly-convex quadratic program.This suits well our scheme formulation on drift-free redundancy-resolution of robots.The dual neural network is then simulated based on the five-link planar robot manipulator,which substantiates the effectiveness of the joint-angle-drift-free neural resolution scheme.
Yunong Zhang Zhiguo Tan Zhi Yang Xuanjiao Lv
Department of Electronics and Communication Engineering Sun Yat-Sen University SYSU Guangzhou 510275,Guangdong Province,China
国际会议
2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)
张家界
英文
1274-1279
2008-06-20（万方平台首次上网日期，不代表论文的发表时间）