A novel zeroing neural network for dynamic sylvester equation solving and robot trajectory tracking

Abstract

To solve the theoretical solution of dynamic Sylvester equation (DSE), we use a fast convergence zeroing neural network (ZNN) system to solve the time-varying problem. In this paper, a new activation function (AF) is proposed to ensure fast convergence in predefined times, as well as its robustness in the presence of external noise perturbations. The effectiveness and robustness of this zeroing neural network system is analyzed theoretically and verified by simulation results. It was further verified by the application of robotic trajectory tracking.