Discrete-Time Zeroing Neural Network for Time-Dependent Constrained Nonlinear Equation With Application to Dual-Arm Robot System

Dongsheng Guo, Yilin Yu, Naimeng Cang, Zehua Jia, Weidong Zhang, Zhisheng Ma

Abstract: Constrained nonlinear equations (CNEs) are involved in numerous practical applications, and many solutions to CNEs have been reported. In particular, a special neural network called zeroing neural network (ZNN) has recently been developed to solve the time-dependent CNE (TDCNE). In this study, we propose a new discrete-time ZNN (DTZNN) model to determine the numerical solution of the TDCNE. Such a model, which is derived from the discretization of the previous ZNN model via a special difference formula, can achieve excellent performance on computing and solving the TDCNE. Theoretical analysis and comparative numerical results further denote the validity and superiority of the proposed DTZNN model. Finally, the proposed model is used to simulate the dual-arm robot system, which verifies the practicability and feasibility of the DTZNN.