Robust Neural Dynamics for Depth Maintenance Tracking Control of Robot Manipulators With Uncertainty and Perturbation

Abstract

The existence of inner uncertainty and external perturbation usually becomes a hindrance for the effective time-variant control of robot manipulators. Both the robustness and convergence property are regarded as two significant issues to be addressed for preferred solutions to robot manipulators. To handle the time-variant motion control of robot manipulators in the presence of both uncertainty and perturbation, a robust recurrent neural network (RRNN) model with definable convergence time (DCT) property is proposed in this paper. Theoretical analysis based on Lyapunov theory rigorously proves that the proposed RRNN model inherently possesses the global stability, robustness and time efficiency. The solution synthesized via the proposed model with uncertainty and perturbation shows desirable time-variant control performance, i.e., faster convergence and higher accurate. In addition, detailed path-tracking examples, performance comparisons, visual-assisted depth maintenance tracking control demonstrations, and extensive tests by applying both PUMA 560 and INNFOS are presented to validate the effectiveness and superiority of the proposed RRNN model for time-variant control of robot manipulators. Note to Practitioners—This article addresses the issue of uncertainty in robot information, a common occurrence in real-time robot learning and control. This paper presents a precise, efficient, and stable solution that leverages real-time feedback information to resolve real-time control problems for robotic manipulators at the velocity level. Additionally, the paper provides a comprehensive overview of the algorithmic steps and theoretical foundations of the RRNN model to facilitate understanding. To validate the effectiveness and superiority of the proposed approach, the study conducts computer simulations and comparisons using actual parameters and models. Finally, an application to the depth maintenance trecking control of robot mainpulators provides an applicative demo of the porposed neural dynamics for practitioners.