Open AccessResearch on Manipulator Tracking Control Algorithm Based on RBF Neural Network
Author(s) -
Zhoulin Chang,
Linzhao Hao,
Qiyan Yan,
Tian-Yu Ye
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/3/032072
Subject(s) - artificial neural network , computer science , tracking (education) , control theory (sociology) , trajectory , nonlinear system , controller (irrigation) , control (management) , control engineering , parallel manipulator , fault tolerance , artificial intelligence , robot , engineering , psychology , pedagogy , physics , quantum mechanics , astronomy , agronomy , biology , distributed computing
According to the characteristics of strong coupling and highly nonlinear of manipulator, a trajectory tracking control method based on neural network is proposed. This paper makes full use of the neural network’s self-learning characteristics, parallel processing ability, nonlinear mapping ability, fault tolerance and so on, and combines it with other control methods to design a controller which can improve the tracking performance of manipulator. The simulation results show that the control method can improve the effectiveness and accuracy of robot arm trajectory tracking.