Inertial Zeroing Neural Network for Robot Manipulator Control Described by Different-Level Dynamic Linear System

Enhancing the stability and precision of robot manipulator control represents a pivotal focus in engineering. In this paper, we focus on studying how to optimize the stability and accuracy of robot manipulator control. The proposed approach merges an inertial neural network with a discrete zeroing neural network, resulting in a remarkable reduction of shaking and error. The discrete zeroing neural network model is proposed in the basis of the different-level dynamic linear system. However, this model has the disadvantage of significant oscillations in solving different-level dynamic linear systems with great joint angle jitter and derivative of the change in joint values, which may result in hardware damage and control errors. In order to solve this problem, we put forward an inertial zeroing neural network. The tracking error of the inertial zeroing neural network is smaller than the tracking error of the general zeroing neural network. In addition, the joint angle jitter of the inertial zeroing neural network is smaller and the robot manipulator changes more smoothly at the initial phase. The results of the simulation confirm the superiority of the initial zeroing neural network.