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

被引：176
|
作者：
Yang, Chenguang [1 ]
Huang, Dianye [1 ]
He, Wei [2 ,3 ]
Cheng, Long [4 ,5 ]
机构：
[1] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[3] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
[4] Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
[5] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2021年 / 32卷 / 09期
关键词：
Manipulators; Lyapunov methods; Robot kinematics; Trajectory tracking; Automation; Barrier Lyapunov function (BLF); constrained control; input saturation; robot manipulator; NONLINEAR-SYSTEMS;
D O I：
10.1109/TNNLS.2020.3017202
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：4231 / 4242
页数：12
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