A novel fixed-time zeroing neural network and its application to path tracking control of wheeled mobile robots

Abstract

Based on the current fixed-time stability criteria, a new Lyapunov function is designed to achieve fixed-time stability for the nonlinear dynamical system. It contains an exponential function term which can make the convergence rate faster. This paper gives the proof of our fixed-time stability criterion and estimates the upper bound of convergence time. The upper bound of convergence time is relatively smaller because it is a constant compounded by a two-layer logarithmic function. While, the impact of parameters is analyzed and some strategies for parameter selection are provided. On the basis of this achievement, we give a novel fixed-time zeroing neural network and it is applied into the wheeled mobile robot path tracking problem. Lastly, simulation results show the validity of our methods.