Gaussian Process-Based Adaptive Sliding Mode Tracking Control for Robotic Manipulators

被引：0
|
作者：
Li, Tong [1 ,2 ]
Sun, Liang [1 ,2 ]
Jiang, Jingjing [3 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
[3] Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, Leics, England
来源：
2022 WRC SYMPOSIUM ON ADVANCED ROBOTICS AND AUTOMATION, WRC SARA | 2022年
基金：
中国国家自然科学基金;
关键词：
data-driven model; adaptive sliding mode; tracking control; robotic manipulators;
D O I：
10.1109/WRCSARA57040.2022.9903974
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A Gaussian process (GP) based adaptive sliding mode control algorithm is proposed for tracking control of robotic manipulators with dynamical uncertainties. Gaussian process is used to obtain a data-driven model which can compensate for the unknown dynamics of the system.The sliding mode controller with parameter adaptive law is designed to bring the trajectory to the normalized tracking and to force the trajectory close to the sliding surface. Stability analysis based on the Lyapunov theorem is given to guarantee the tracking control performance. Numerical simulations on a 2-DOF robotic manipulator verify the effectiveness of the proposed controller.
引用
收藏
页码：209 / 214
页数：6
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