A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models
Ning Tan, Peng Yu, Zhiyan Zhong, Fenglei Ni
Abstract
Taking advantage of their inherent dexterity, robotic arms are competent in completing many tasks efficiently. As a result of the modeling complexity and kinematic uncertainty of robotic arms, model-free control paradigm has been proposed and investigated extensively. However, robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying. In this paper, we first propose a new kind of zeroing neural network (ZNN), i.e., integration-enhanced noise-tolerant ZNN (IENT-ZNN) with integration-enhanced noise-tolerant capability. Then, a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms, which improves the performance of robotic arms with the disturbance of noise, without knowing the structural parameters of the robotic arms. The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis. Finally, simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.