scholarly journals RBF Neural Network Based Correction Iterative Learning Control for Direct-drive Pump-controlled Clutch Actuator

2020 ◽  
Vol 787 ◽  
pp. 012023
Author(s):  
Jieyu Wang ◽  
Bingzhao Gao ◽  
Hong Chen
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Rbf Neural Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Direct Drive ◽  
Drive Pump

Iterative Learning Control Based on Radial Basis Function Network for Exoskeleton Arm

2011 ◽  
Vol 415-417 ◽  
pp. 116-122 ◽  
Author(s):  
Jie Liu ◽  
Yu Wang ◽  
He Ting Tong ◽  
Ray P.S. Han
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Iterative Learning Control ◽  
Rbf Neural Network ◽  
Radial Basis Function Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Radial Basis

In this paper, we propose iterative learning control (ILC) scheme for exoskeleton arm driven by pneumatic artificial muscles (PAM), with special and unknown parameters, performing repetitive tasks. This desired control input of ILC was estimated by radial basis function (RBF) neural network incorporated experience database. An ILC controller, which uses the position of the joint where an angular sensor is used as the input of the ILC controller, is developed and tested on exoskeleton arm under well controlled conditions. RBF neural network was proposed to obtain the initial value of ILC. The experiment result on the experimental platform show that the algorithm is successful also in the application of exoskeleton arm.

A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 1225-1229
Author(s):  
Heng Jie Li ◽  
Xiao Hong Hao ◽  
Xi Ping Pei
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Rbf Neural Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration; another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.

Pulse neural network–based adaptive iterative learning control for uncertain robots

2012 ◽  
Vol 23 (7-8) ◽  
pp. 1885-1890 ◽  
Author(s):  
Xiongxiong He ◽  
Hualiang Zhuang ◽  
Duan Zhang ◽  
Zhenhua Qin
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Adaptive Iterative Learning Control
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