Piecewise Bilinear models for feedback error learning: On-line feedforward controller design

2013 ◽  
Author(s):  
Luka Eciolaza ◽  
Tanadari Taniguchi ◽  
Michio Sugeno ◽  
Dimitar Filev ◽  
Yan Wang
Keyword(s):  
Controller Design ◽  
Bilinear Models ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
On Line ◽  
Feedforward Controller ◽  
Error Learning

scholarly journals Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Takashi Watanabe ◽  
Yoshihiro Sugi
Keyword(s):  
Computer Simulation ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Wrist Joint ◽  
Training Data ◽  
Joint Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Feedforward Controller ◽  
Error Learning

Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device.

scholarly journals Applying RBF Neural Nets for Position Control of an Inter/Scara Robot

2009 ◽  
Vol 4 (2) ◽  
pp. 148
Author(s):  
Fernando Passold
Keyword(s):  
Neural Networks ◽  
Position Control ◽  
Sliding Mode ◽  
Neural Nets ◽  
Rbf Neural Networks ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
On Line ◽  
Error Learning ◽  
Manipulator Robot

This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. These approach has performed very successfully, with better results obtained with the RBF networks when compared to PID and sliding mode positional controllers.

On-line adaptive control for inverted pendulum balancing based on feedback-error-learning

2007 ◽  
Vol 70 (4-6) ◽  
pp. 770-776 ◽  
Author(s):  
Xiaogang Ruan ◽  
Mingxiao Ding ◽  
Daoxiong Gong ◽  
Junfei Qiao
Keyword(s):  
Adaptive Control ◽  
Inverted Pendulum ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
On Line ◽  
Error Learning

Nonlinear Discrete-time feedback error learning with PI Controller for AC servo motor

2008 ◽  
Author(s):  
Noppanan Suwanjatuporn ◽  
Mes Napaamporn ◽  
Waree Kongprawechnon ◽  
Sirisak Wongsura
Keyword(s):  
Discrete Time ◽  
Pi Controller ◽  
Servo Motor ◽  
Ac Servo ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Ac Servo Motor
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