Novel Data-Driven Repetitive Motion Control Scheme for Redundant Manipulators With Zeroing Neurodynamics

Abstract

Repetitive motion control of redundant manipulators typically requires precise kinematic models to construct Jacobian matrices. However, model-based approaches are inherently limited when manipulator parameters are unavailable or only partially known. This paper introduces a novel data-driven discrete zeroing neurodynamics (DDZN) model for repetitive motion control. Specifically, a Jacobian matrix estimation method based on data-driven technology is proposed, which eliminates the need for prior models by leveraging historical input-output information. By integrating the Jacobian matrix estimation with a discrete zeroing neurodynamics (DZN) model, the approach enables simultaneous trajectory tracking and repeatable configuration recovery without relying on structural parameters. Theoretical analysis verifies the performance of DDZN model under noise environment. Furthermore, abundant experiment results validate its reliability and superior performance compared with various models.