典型文献
Adaptive Neural Network Control for Euler-Lagrangian Systems with Uncertainties
文献摘要：
An adaptive controller involving a neural network (NN) compensator is proposed to resist the uncertainties in the Euler-Lagrangian system ( ELS ). Firstly, a proportional-differential(PD) control law is designed for the nominal model. Meanwhile, the uncertainties including model error and external disturbance are separated from the closed-loop system. Then, an adaptive NN compensator based on the online training mode is proposed to eliminate the adverse effect of the uncertainties. In addition, the stability of the closed-loop system is proved by Lyapunov theory. Finally, the effectiveness of the proposed approach is verified on a two-degree-of-freedom robot manipulator.
文献关键词：
中图分类号：
作者姓名：
CHENG Xin;LU Wenke;LIU Huashan
作者机构：
College of Information Science and Technology,Donghua University,Shanghai 201620,China;Engineering Research Center of Digitized Textile and Fashion Technology,Ministry of Education,Shanghai 201620,China
文献出处：
引用格式：
[1]CHENG Xin;LU Wenke;LIU Huashan-.Adaptive Neural Network Control for Euler-Lagrangian Systems with Uncertainties)[J].东华大学学报（英文版）,2022(05):485-489
A类：
B类：
Adaptive,Neural,Network,Control,Euler,Lagrangian,Systems,Uncertainties,An,adaptive,controller,involving,neural,network,NN,compensator,proposed,resist,uncertainties,system,ELS,Firstly,proportional,differential,law,designed,nominal,model,Meanwhile,including,error,external,disturbance,are,separated,from,closed,loop,Then,online,training,eliminate,adverse,In,addition,stability,proved,by,Lyapunov,theory,Finally,effectiveness,approach,verified,degree,freedom,robot,manipulator
AB值：
0.626891
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