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具有轨迹跟踪约束和输入饱和的机器人操纵器的神经控制。

Neural Control of Robot Manipulators With Trajectory Tracking Constraints and Input Saturation.

出版信息

IEEE Trans Neural Netw Learn Syst. 2021 Sep;32(9):4231-4242. doi: 10.1109/TNNLS.2020.3017202. Epub 2021 Aug 31.

DOI:10.1109/TNNLS.2020.3017202
PMID:32857705
Abstract

This article presents a control scheme for the robot manipulator's trajectory tracking task considering output error constraints and control input saturation. We provide an alternative way to remove the feasibility condition that most BLF-based controllers should meet and design a control scheme on the premise that constraint violation possibly happens due to the control input saturation. A bounded barrier Lyapunov function is proposed and adopted to handle the output error constraints. Besides, to suppress the input saturation effect, an auxiliary system is designed and emerged into the control scheme. Moreover, a simplified RBFNN structure is adopted to approximate the lumped uncertainties. Simulation and experimental results demonstrate the effectiveness of the proposed control scheme.

摘要

本文提出了一种考虑输出误差约束和控制输入饱和的机器人机械手轨迹跟踪控制方案。我们提供了一种替代方法,可以消除大多数基于 BLF 的控制器应满足的可行性条件,并在由于控制输入饱和可能导致约束违反的前提下设计控制方案。提出并采用有界障碍李雅普诺夫函数来处理输出误差约束。此外,为了抑制输入饱和效应,设计并引入了一个辅助系统到控制方案中。此外,采用简化的 RBFNN 结构来逼近集中不确定性。仿真和实验结果证明了所提出的控制方案的有效性。

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