Discrete Data-Driven Control of Redundant Manipulators With Adaptive Jacobian Matrix

Abstract

Redundant manipulators are widely used in various fields due to their multiple degrees of freedom characteristics, and their tracking control is an important problem in the field of robotics. In order to control manipulators with unknown models in practical applications, this article proposes a discrete data-driven Jacobian matrix adaptive control (DDJMAC) scheme. The scheme is composed of a discrete Jacobian matrix estimator, a discrete neural dynamics controller, and a Kalman filter. Subsequently, the convergence and robustness of the DDJMAC scheme are demonstrated by theoretical analyses. Finally, simulations, comparisons, and physical experiments are performed on redundant manipulators, and the results confirm the effectiveness, superiority, and practicality of the proposed scheme.