Joint angle drift problem of PUMA560 robot arm solved by a simplified LVI-based primal-dual neural network

Abstract

A simplified primal-dual neural network based on linear variational inequalities (LVI) is presented in this paper, which is used to solve the joint angle drift problem of PUMA560 robot arm. To do this, a drift-free criterion is exploited in the form of a quadratic function. In addition, the physical constraints such as joint limits and joint velocity limits are incorporated into the problem formulation of such a scheme. The scheme is finally reformulated as a strictly-convex quadratic-programming (QP) problem and resolved at joint-velocity level. As a QP real-time solver, the simplified LVI-based primal-dual neural network is developed based on the QP-LVI conversion and Karush-Kuhn-Tucker (KKT) conditions. It has a simple piecewise-linear dynamics and global exponential convergence to the optimal solution of strictly-convex quadratic-program. The simplified LVI-based primal-dual neural network is simulated based on PUMA560 robot manipulator, and effectively remedies the joint angle drift problem of PUMA560 robot.