Application of the Neural Network for a Hydraulic Motor/Load System. 2nd Report. Precise Angular Position Control of Hydraulic Motor/Load System Using PWM Signals within the Inherent Dead Zone of the System.

2002 ◽  
Vol 68 (670) ◽  
pp. 1827-1833 ◽  
Author(s):  
Hiroshi KATOH ◽  
Takao NISHIUMI ◽  
Shizurou KONAMI
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Dead Zone ◽  
Angular Position ◽  
Hydraulic Motor ◽  
The Neural Network ◽  
Motor Load

scholarly journals Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2576
Author(s):  
Alfonso Gómez-Espinosa ◽  
Roberto Castro Sundin ◽  
Ion Loidi Eguren ◽  
Enrique Cuan-Urquizo ◽  
Cecilia D. Treviño-Quintanilla
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Control System ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Position Control ◽  
Reference Signal ◽  
Angular Position ◽  
Hysteresis Model ◽  
Direct Control

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83° was achieved when validated against several set-points within the possible range.

Position Control of Ultrasonic Motors Using Dead-Zone Compensation with Fuzzy Neural Network

2006 ◽  
Vol 34 (11) ◽  
pp. 1253-1266 ◽  
Author(s):  
Tomohiro Yoshida ◽  
Tomonobu Senjyu ◽  
Mitsuru Nakamura ◽  
Naomitsu Urasaki ◽  
Hideomi Sekine ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Position Control ◽  
Dead Zone ◽  
Ultrasonic Motors ◽  
Fuzzy Neural

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

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