Research on Manipulator Tracking Control Algorithm Based on RBF Neural Network | Zendy

Zhoulin Chang | Zendy; Linzhao Hao | Zendy; Qiyan Yan | Zendy; Tian-Yu Ye | Zendy

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Research 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.

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