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

Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid the task failure affected by the periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. First, in this article, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Second, the QP problem is converted into a matrix equation. Third, based on the 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.

Automated summary

This article introduces the use of a circadian rhythms neural network (CRNN) to solve motion planning problems of redundant robot manipulators suffering from periodic noise, showing better robustness and performance than traditional neural networks. Comparative simulations and physical experiments further validate the effectiveness and practicality of the proposed CRNN method.