A robust controller design for networked hydraulic pressure control system based on CPR

2019 ◽  
Vol 12 (6) ◽  
pp. 1651-1661
Author(s):  
Wei Shen ◽  
Jiehao Wang
Keyword(s):  
Control System ◽  
Controller Design ◽  
Pressure Control ◽  
Hydraulic Pressure ◽  
Robust Controller ◽  
Pressure Control System ◽  
Robust Controller Design

scholarly journals RENOVATION OF NMIJ'S 5 MN HYDRAULIC AMPLIFICATION TYPE FORCE STANDARD MACHINE

2013 ◽  
Vol 24 ◽  
pp. 1360011
Author(s):  
TOSHIYUKI HAYASHI ◽  
HIROSHI MAEJIMA ◽  
KAZUNAGA UEDA ◽  
MITSUHARU AOKITSU
Keyword(s):  
Control System ◽  
Amplification Factor ◽  
Pressure Gauge ◽  
Pressure Control ◽  
Force Transducer ◽  
Hydraulic Pressure ◽  
Programmable Logic ◽  
Pressure Control System ◽  
Type Pressure ◽  
Measurement Repeatability

The 5 MN hydraulic amplification type force standard machine was renovated by replacing its hydraulic pressure control system, measuring ram and cylinder. For re-evaluation of uncertainty of force amplification factor, diameters of the main and mearsuring rams and cylinders were re-measured. A programmable logic controller with a personal computer is used to control the weight motion and the valve actuation, to monitor various kinds of sensors output, and to record readings of a force transducer under calibration. Fluctuation of calibration force was suppressed by using a quartz-resonance type pressure gauge, and measurement repeatability was improved to some extent. However, there remains room for improvement to further stabilize the calibration force by modifying the measuring ram.

Application of ANN in Hydraulic Pressure Control Fault Diagnosis System

2013 ◽  
Vol 347-350 ◽  
pp. 864-868
Author(s):  
Xiao Yu Zhang ◽  
Li Li Ding
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Pressure Control ◽  
Fault Isolation ◽  
Hydraulic Pressure ◽  
Diagnosis System ◽  
Pressure Control System ◽  
Fault Diagnosis System ◽  
Artificial Neural Network Ann

The existing hydraulic pressure control fault diagnosis system is effective on fault detection, but the fault isolation capability is bad. In order to improve the capability of the fault isolation, the artificial neural network (ANN) is used in the fault diagnosis system. Aimed at the representative diagnosis of the hydraulic pressure control system, the three layers feedback network is adopted, the basic theory of conjugate gradient BP neural network is explained in detail, and the key techniques are introduced. Five types of typical faults of hydraulic pressure control system can be distinguished easily by it, the faults diagnosis efficiency is higher 30% than ever and the fault diagnosis capability is better 80% than before.

Sign in / Sign up

Export Citation Format

Share Document