A Circadian Rhythms Neural Network for Solving Redundant Robot Manipulators Tracking Problem Perturbed by Periodic Noise

Abstract

Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid task failure affected by periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. Firstly, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Secondly, the QP problem is converted into a matrix equation. Thirdly, based on neural dynamic design method, a CRNN model is exploited and developed to solve the matrix equation of the tracking problem. Comparative simulations between the proposed CRNN and the traditional zeroing neural network show that the CRNN has better robustness and performance on solving the end-effector tracking task. Two physical experiments are conducted to further certify the effectiveness, robustness and practicability of the proposed CRNN.