Networked control system fault detection method based on neural network prediction

A networked control and neural network technology, applied in the field of power system and industrial process control, can solve the problems of inability to directly obtain the observer parameters, unstable operation, and system performance degradation.

Active Publication Date: 2016-12-21
HENAN POLYTECHNIC UNIV
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] The networked control system has the advantages of low cost, strong reliability, and flexible structure. It is widely used in telemedicine, robotics, aerospace and other fields. However, the introduction of the network is prone to problems such as time delay and packet loss. These phenomena will not only make the system performance degradation and even make it run unstable[1,2]
With the gradual expansion of the network scale, the stability and security requirements of the system are gradually increasing. Therefore, the fault detection problem of the networked control system has been widely concerned and studied by experts and scholars. Aiming at the problems existing in the operation of the current networked control system, the current For short-delay networked control systems, the sufficient conditions for system stability are given by constructing the Lyapunov function and using the linear matrix inequality (LMI) method; considering the filter design problem of the network system with time delay and random packet loss, the LMI method is used to The met

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Networked control system fault detection method based on neural network prediction
  • Networked control system fault detection method based on neural network prediction
  • Networked control system fault detection method based on neural network prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0105] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0106] Such as figure 1 -shown in -5, a networked control system fault detection method based on neural network prediction, comprising the following steps:

[0107] The first step is to construct the RBF neural network system. Firstly, construct the mathematical model of the networked control system with sensor data random loss and interference, and then establish the traditional RBF neural network for predicting the system output based on the mathematical model of the networked control system. The traditional RBF neural network introduces at least one set of hidden layer functions, the error cost function of the neural network and the efficient prediction output value operation function to optimize the traditional RBF neural network and obtain hi...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a networked control system fault detection method based on neural network prediction, which comprises four steps of RBF neural network system building, system fault detection function building, system stability judgment and operation and system fault judgment and operation function building. The system building and operation process is simple, the operation efficiency and the operation precision are relatively high, an improved RBF neural network prediction controller is adopted to effectively predict system output data information, and thus, bad influences on the system by packet loss can be effectively cancelled, errors are smaller and training times are reduced through adjusting learning efficiency on the basis of adopting feedback correction on the obtained predicted output value for correction, and better convergence and quicker prediction speed can be obtained. Meanwhile, when fault happens to the system, happening of the fault can be quickly detected according to a designed fault observer and a judgment criterion.

Description

technical field [0001] The invention relates to a networked control system fault detection method based on neural network prediction, which belongs to the technical field of power system and industrial process control. Background technique [0002] The networked control system has the advantages of low cost, strong reliability, and flexible structure. It is widely used in telemedicine, robotics, aerospace and other fields. However, the introduction of the network is prone to problems such as time delay and packet loss. These phenomena will not only make the system Performance drops and even makes it run unstable [1,2]. With the gradual expansion of the network scale, the stability and security requirements of the system are gradually increasing. Therefore, the fault detection problem of the networked control system has been widely concerned and studied by experts and scholars. Aiming at the problems existing in the operation of the current networked control system, the curre...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 钱伟杨蒙蒙王瑞王俊峰李冰锋
Owner HENAN POLYTECHNIC UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products