An interference-tolerant fast convergence zeroing neural network for dynamic matrix inversion and its application to mobile manipulator path tracking

In this paper, a new interference-tolerant fast convergence zeroing neural network (ITFCZNN) using a novel activation function (NAF) for solving dynamic matrix inversion (DMI) is presented and investigated. Compared with the original zeroing neural network (OZNN) models, the proposed ITFCZNN not only has the ability to converge to 0 within a fixed-time, but also resist different types of interference and noises in solving DMI problems. Besides, detailed mathematical analysis of convergence and robustness of the ITFCZNN are provided. Comparative numerical simulation verifications of the new ITFCZNN and the OZNN activated by other commonly used activation functions (AF) are also provided to demonstrate the better robustness, effectiveness and fixed-time convergence of the ITFCZNN. In addition, a mobile manipulator path tracking application example is given to verify the applicability and feasibility of the ITFCZNN with interference and noises. Both of the theoretical analysis and numerical simulation results verify the effectiveness and robustness of the ITFCZNN model. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Fat Lilly of Engineering, Alexandria University.

Automated summary

This paper introduces a new interference-tolerant fast convergence zeroing neural network model ITFCZNN using a novel activation function to solve dynamic matrix inversion problems. The model not only has fast convergence and interference resistance, but also demonstrates robustness and effectiveness in different environments through numerical simulation verification.