Comparison of feedback controllers for feedback-error-learning neural network control system with application to a flexible micro-actuator

2002 ◽  
Author(s):  
M. Sasaki ◽  
M. Kawafuku ◽  
K. Takahashi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System ◽  
Feedback Controllers ◽  
Micro Actuator ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Backstepping sliding mode neural network control system for hypersonic vehicle

2019 ◽  
Vol 27 (11) ◽  
pp. 2392-2401
Author(s):  
刘 蓉 LIU Rong ◽  
黄大庆 HUANG Da-qing ◽  
姜定国 JIANG Ding-guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Sliding Mode ◽  
Hypersonic Vehicle ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System

Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process

2010 ◽  
Vol 426-427 ◽  
pp. 220-224
Author(s):  
X.M. Li ◽  
Ning Ding
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Grinding Process ◽  
Neural Network Control ◽  
Network Control System ◽  
Cylindrical Grinding ◽  
Adaptive Fuzzy Neural Network ◽  
Fuzzy Neural

An adaptive fuzzy neural network control system in cylindrical grinding process was proposed. In this system, the initial cylindrical grinding parameters were decided by the expert system based on fuzzy neural network. Multi-feed and setting overshoot optimization methods were also adopted during the grinding process, and a human machine cooperation system (composed of human and two fuzzy – neural networks) could revise the process parameters in real-time. The experiment of the cylindrical grinding was implemented. The results showed that this control system was valid, and could greatly improve the cylindrical grinding quality and machining efficiency.

Vibration Control of the Piping System: A Method Using Feedback-Error-Learning

1995 ◽  
Author(s):  
Yoshihiro Takita
Keyword(s):  
Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Method ◽  
Piping System ◽  
Processing Unit ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Lq Control

Abstract This paper presents a vibration control method for piping systems using a feedback control system constructed with LQ-control and a neural network featuring feedback-error learning. The piping system is normally flexible, therefore, natural frequencies of the system fluctuate variably when the density of the content. This paper shows that the piping system changes dynamics according to increases or decreases of the mass effects. In order to reduce the first vibration mode of the piping system without spillover instability, the control system is designed using LQ-control with feedback-error-learning applied to an adapted nonlinear feedback controller. The effectiveness of this control method is confirmed by the neural network simulation program named NeuroLab and is experimented using data measured by the control system constructed with the digital signal processing unit.

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