Integral Separation PID Control of Certain Electro-hydraulic Servo System based on RBF Neural Network Supervision

The working principle of the electrical-hydraulic servo system for steel strip deviation is introduced. The mathematical model of the system is established, and the performance indexes in time domain are analyzed with MATLAB. Aiming at the steel strip deviation, an improved PID control method based on RBF neural network is proposed. According to Jacobian information identification of RBF neural network combined with incremental PID algorithm, the self-tuning of parameters is implemented so that the performance of the system can achieve the designed requirements. Compared with traditional PID control, the simulation results show RBF- PID control has better dynamic performance and stabilization.

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Research on Electro-Hydraulic Servo System Based on BP-RBF Neural Network

Advances in Mechanical Design - Mechanisms and Machine Science ◽

10.1007/978-981-32-9941-2_50 ◽

2019 ◽

pp. 615-622

Author(s):

Tao Chen ◽

Wenqun Zhang ◽

Jianggui Han

Keyword(s):

Neural Network ◽

Rbf Neural Network ◽

Servo System ◽

Hydraulic Servo ◽

Hydraulic Servo System

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.691 ◽

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.

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Diagonal Recurrent Neural Network-Based Electro-Hydraulic Servo System Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.581 ◽

2013 ◽

Vol 336-338 ◽

pp. 581-584 ◽

Cited By ~ 1

Author(s):

Ye Lv ◽

Jing Ma ◽

De Cun He ◽

Xiang Gao

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Pid Control ◽

Servo System ◽

System Structure ◽

System Control ◽

Control Effect ◽

External Interference ◽

Hydraulic Servo ◽

Hydraulic Servo System

The electro-hydraulic servo system gradually processes toward the fast, high-power and high-precision direction. The traditional PID control needs to coordinate the contradiction between rapidity and stability, and cannot meet the system performance requirements in the case of parameter variations and external interference. Based on electro-hydraulic servo system structure and principles, system mathematical model was established, and Diagonal Recurrent Neural Network (DRNN)-based adaptive PID controller was designed and compared with positional PID control. The simulation results show that: DRNN adaptive PID control effect is superior to positional PID control, which can effectively improve the system dynamic and anti-interference performance, and has strong self-learning and adaptive capacity.

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