A Time-Specified Zeroing Neural Network for Quadratic Programming With Its Redundant Manipulator Application

Abstract

In this article, to solve a time-varying quadratic programming with equation constraint, a new time-specified zeroing neural network (TSZNN) is proposed and analyzed. Unlike the existing methods such as the Zhang neural network with different activation functions and a finite-time neural network, the TSZNN model is incorporated into a terminal attractor, and the convergent error can be guaranteed to reduce to zero in advance (instead of the finite-time property). The main advantage of the TSZNN model is that it is independent of the initial state of the systematic dynamics, which is much astonishing to the finite convergence relying on the initial conditions and comprehensively modifies the convergent performance. Mathematical analyses substantiate the prespecified convergence of the TSZNN model and high convergent precision under the situation of various convergent time settings. The prespecified convergence of the TSZNN model for a quadratic programming problem has been mathematically proved under different convergent constant settings. In addition, simulation applications conducted on a repeatable trajectory planning of the redundant manipulator are studied to demonstrate the validity of the proposed TSZNN model.