A novel fixed‐time ZNN model and application to robot trajectory tracking

Abstract

Abstract In order to achieve robot trajectory tracking in fixed‐time, a novel fixed‐time zeroing neural network model is designed. Initially, the inverse kinematic model of robot trajectory tracking is translated into a time‐varying quadratic programs problem. Subsequently, a novel fixed‐time zeroing neural network is proposed for solving the time‐varying quadratic programs problem. Furthermore, the fixed‐time stability of this model is rigorously established, and an upper bound of convergence time, irrespective of the initial point, is estimated. Finally, numerical simulation results underscore the efficacy of the proposed methodologies.