BI-CRITERIA ACCELERATION MINIMIZATION OF REDUNDANT ROBOT MANIPULATORS USING NEW PROBLEM FORMULATION AND LVI-BASED PRIMAL-DUAL NEURAL NETWORK
This paper is aimed at the remedy of a discontinuity problem arising in the infinity-norm acceleration minimization (INAM) of robot manipulators.Three important matters are involved.1) A new acceleration-level bi-criteria scheme is proposed for preventing the INAM solution discontinuities and joint torques instability problem.It combines the minimum infinity-norm and minimum two-norm solutions by using a new problem formulation.2) Such a bi-criteria scheme is then reformulated as a quadratic programming (QP) problem with its coefficient matrix being positive semi-definite.3) The LVI-based primal-dual neural network is finally chosen to solve online such a QP problem as well as the bi-criteria weighting scheme.This is in view of the fact that the LVI-based primal-dual neural network has a simple piecewise-linear dynamics and higher computational efficiency.Simulation results based on PMUA560 robot manipulator also illustrate the advantages of using such a neural weighting scheme proposed in this paper.
Redundant manipulators Bi-criteria acceleration minimization Joint limits avoidance Quadratic programming Online solution Recurrent neural networks
YU-NONG ZHANG JIANG-PING YIN
Department of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou 510275, China
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
715-720
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）