Inverse-Free Discrete ZNN Models for Solving Future Nonlinear Equality and Inequality System With Robot Manipulator Control

Abstract

In this article, future nonlinear equality and inequality system (FNEIS) is first investigated. To solve FNEIS, continuous zeroing equivalency (CZE) model is proposed based on zeroing neural network (ZNN). However, the matrix inversion operation in the model is relatively time-consuming. To reduce the computation time consumption, two inverse-free ways are utilized, and two continuous inverse-free (CIF) models are proposed. For real-time digital implementation of CIF models, a new discretization formula is constructed, and then two types of discrete inverse-free (DIF) models are proposed, i.e. DIF-I and DIF-II models. Both theoretical analyses and numerical experiments demonstrate the effectiveness of two DIF models. Finally, two DIF models are applied to solve the tracking control problem, considering joint angle limits in robot manipulator control. Simulative and physical experiments validate their effectiveness, superiority, and practicality.