Open AccessNeural networks impedance control of robots interacting with environments
Author(s) -
Li Yanan,
Ge Shuzhi Sam,
Zhang Qun,
Lee Tong Heng
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2012.1032
Subject(s) - robot , stability (learning theory) , impedance control , artificial neural network , iterative learning control , control theory (sociology) , computer science , robot control , control engineering , electrical impedance , control (management) , artificial intelligence , mobile robot , engineering , machine learning , electrical engineering
In this study, neural networks (NN) impedance control is proposed for robot–environment interaction. Iterative learning control is developed to make the robot dynamics follow a given target impedance model. To cope with the problem of unknown robot dynamics, NN are employed such that neither the robot structure nor the physical parameters are required for the control design. The stability and performance of the resulted closed‐loop system are discussed through rigorous analysis and extensive remarks. The validity and feasibility of the proposed method are verified through simulation studies.