Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Nonlinear system fault detection and estimation method and device based on adaptive iterative learning algorithm

A nonlinear system and self-adaptive iterative technology, applied in the field of nonlinear system fault detection and estimation, can solve the problems of large estimation error and slow convergence speed, achieve good accuracy, improve convergence speed, and reduce fault estimation error

Inactive Publication Date: 2021-11-09
HENAN UNIVERSITY
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the iterative learning algorithm has problems such as large estimation error and slow convergence speed in the process of nonlinear system fault detection and estimation.

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
  • Nonlinear system fault detection and estimation method and device based on adaptive iterative learning algorithm
  • Nonlinear system fault detection and estimation method and device based on adaptive iterative learning algorithm
  • Nonlinear system fault detection and estimation method and device based on adaptive iterative learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] refer to figure 1 , Embodiment one of the present invention provides a kind of non-linear system fault detection and estimation method based on self-adaptive iterative learning algorithm, described method comprises the following steps:

[0026] Step 1: Establish a nonlinear continuous-time system model.

[0027] The nonlinear continuous-time system model with disturbance is as follows:

[0028]

[0029] where x(t)∈R n is the state of the system, u(t)∈R m is the control input, y(t)∈R p is the output of the system, f(t)∈R q is the fault signal, A, B, C and E are dimension-appropriate matrices, R represents real numbers, n, m, p and q represent dimensions, and g(t,x(t)) represents a continuous nonlinear vector function, assuming here g(t,x(t)) satisfies the Lipschitz condition, and there is a Lipschitz constant L g makes:

[0030] ||g(t,x 2 (t)-g(t,x 1 (t))||≤L g ||x 2 (t)-x 1 (t)||

[0031] The above model is based on the following assumptions:

[0032] A...

Embodiment 2

[0144] refer to figure 2 , corresponding to the method in Embodiment 1, Embodiment 2 of the present invention provides a nonlinear system fault detection and estimation device based on an adaptive iterative learning algorithm, the device includes: a building module, a design module, a first solution module, calculation module and second solver module. The various modules are specifically used for:

[0145] The building module is used for building a nonlinear continuous time system model.

[0146] The nonlinear continuous-time system model with disturbance is as follows:

[0147]

[0148] where x(t)∈R n is the state of the system, u(t)∈R m is the control input, y(t)∈R p is the output of the system, f(t)∈R q is the fault signal, A, B, C and E are dimension-appropriate matrices, R represents real numbers, n, m, p and q represent dimensions, and g(t,x(t)) represents a continuous nonlinear vector function, assuming here g(t,x(t)) satisfies the Lipschitz condition, and th...

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 nonlinear system fault detection and estimation method and device based on an adaptive iterative learning algorithm. The method comprises the following steps: establishing a nonlinear continuous time system model; based on the nonlinear continuous time system model, designing a fault estimation observer of the system in continuous time; applying a fourth-order Runge-Kutta algorithm to the fault estimation observer to solve an observation system state differential equation; calculating a system state and an output estimation error of the kth iterative learning algorithm based on the solving result, calculating a virtual fault through a fault estimation algorithm, and determining a starting condition of a fault estimation observer; and using an H infinity method, designing a constraint inequality according to the bounded real lemma and the influence on the fault change rate, and solving a learning gain parameter matrix. By means of the method, the fault estimation error is effectively reduced, and the convergence speed of the fault estimation observer is improved.

Description

technical field [0001] The present invention relates to the technical field of fault diagnosis and estimation, in particular to a nonlinear system fault detection and estimation method and device based on an adaptive iterative learning algorithm. Background technique [0002] In recent years, the research on fault diagnosis of complex systems has become a hot topic nowadays, among which the research on fault diagnosis of nonlinear systems has achieved certain theoretical results. In the fault diagnosis of complex systems, if the fault diagnosis model is known, the fault diagnosis method based on this model can detect the fault more accurately. The fault diagnosis of closed-loop system and nonlinear system is the difficulty and hot spot of current research. With the increase of the complexity of the control system, the fault diagnosis of the nonlinear system becomes one of the difficult problems urgently to be solved in the industrial process control. Therefore, the study o...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0243G05B2219/24065
Inventor 侯彦东李雅姚莉孙行行陈政权
Owner HENAN UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com