FAULT DETECTION OF HYDRAULIC SERVO-SYSTEM BASED ON DYNAMIC NEURAL NETWORK

2002 ◽  
Vol 38 (03) ◽  
pp. 46 ◽  
Author(s):  
Ruoqing Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Servo System ◽  
Dynamic Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Fault Detection and Isolation for Hydraulic Servo System Based on Adaptive Threshold and SOM Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 691-697
Author(s):  
Yu Jie Cheng ◽  
Chen Lu ◽  
Li Mei Wang ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Rbf Neural Network ◽  
Servo System ◽  
Adaptive Threshold ◽  
Residual Error ◽  
Error Signal ◽  
Hydraulic Servo ◽  
Som Neural Network ◽  
Hydraulic Servo System

A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

Adaline neural network-based adaptive inverse control for an electro-hydraulic servo system

2011 ◽  
Vol 17 (13) ◽  
pp. 2007-2014 ◽  
Author(s):  
Jianjun Yao ◽  
Xiancheng Wang ◽  
Shenghai Hu ◽  
Wei Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Servo System ◽  
Inverse Model ◽  
Lms Algorithm ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
The Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electro-hydraulic servo system. The system inverse model identifier is constructed by neural network. The task is accomplished by generating a tracking error between the input command signal and the system response. The weights of the neural network are updated by the error signal in such a way that the error is minimized in the sense of mean square using (LMS) algorithm and the neural network is close to the system inverse model. The above steps make the gain of the serial connection system close to unity, realizing waveform replication function in real-time. To enhance its convergence and robustness, the normalized LMS algorithm is applied. Simulation in which nonlinear dead-zone is considered and experimental results demonstrate that the proposed control scheme is capable of tracking desired signals with high accuracy and it has good real-time performance.

scholarly journals Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Shu-Min Lu ◽  
Dong-Juan Li
Keyword(s):  
Neural Network ◽  
State Constraints ◽  
Servo System ◽  
Network Control ◽  
Time Varying ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

Electro-Hydraulic Servo Control Based on CMAC Neural Network for Excavating Robot

2010 ◽  
Vol 34-35 ◽  
pp. 825-830
Author(s):  
Qun Liang Dai ◽  
Hong Liang Dai ◽  
Xiao Hai Qu
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Strong Coupling ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Servo Control ◽  
Cmac Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

In this paper the electric-hydraulic servo system for excavating robot is analysed. The kinematic and the dynamic model of working equipment are established. Aim at the electric-hydraulic servo system of the feature with many variables, strong coupling and non-linear, the CMAC neural network was presented combined with popular PD algorithm, which could realize intelligent control for the working equipment of excavating robot. The result of simulation show that control strategy features higher precision and robustness.

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