Bi-Criteria Acceleration Minimization of Redundant Robot Manipulators using New Problem Formulation and LVI-Based Primal-Dual Neural Network

Abstract

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.