Adaptive Sliding Mode Neural Network-Based Composite Control of Robot Manipulators for Trajectory Tracking

被引：0
|
作者：
Wang, Xingbo [1 ]
Qian, Jidong [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Coll Automat, Coll Artificial Intelligence, Nanjing 210046, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
关键词：
Robot manipulator; sliding mode control; RBF neural network; adaptive; WAVE ENERGY CONVERTERS; PREDICTIVE CONTROL; QUADROTOR UAV; DESIGN; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an adaptive sliding mode control for a robot manipulator based on RBF neural network is proposed. A new form of fast terminal sliding mode is used in this approach. Neural networks is developed to estimate unknown terms of the robot manipulator. The tracking performance of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results verify the nonsingular fast terminal sliding mode controller can achieve faster and higher precision performance in comparison with the conventional continuous sliding mode control method.
引用
收藏
页码：434 / 439
页数：6
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