A Finite Time Adaptive Backstepping Control Method of Flexible Joint Manipulator Based on Neural Network

A flexible joint and neural network technology, which is applied in the field of finite-time adaptive backstepping control of flexible joint manipulators, and can solve problems such as the inability to guarantee the convergence of state error variables.

Active Publication Date: 2020-07-31
ZHEJIANG UNIV OF TECH
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The application of the control method of the above control strategy has certain limitations, or it cannot guarantee that each state error variable converges within a finite time, or the system model must be known

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
  • A Finite Time Adaptive Backstepping Control Method of Flexible Joint Manipulator Based on Neural Network
  • A Finite Time Adaptive Backstepping Control Method of Flexible Joint Manipulator Based on Neural Network
  • A Finite Time Adaptive Backstepping Control Method of Flexible Joint Manipulator Based on Neural Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0077] The present invention will be further described below in conjunction with the accompanying drawings.

[0078] refer to Figure 1-Figure 7 , a finite-time self-adaptive backstepping control method for a flexible joint manipulator based on a neural network, the control method comprising the following steps:

[0079] Step 1, establish the dynamic model of the flexible joint manipulator, initialize the system state, sampling time and control parameters, the process is as follows:

[0080] 1.1 The dynamic model expression of an n-order flexible joint manipulator is:

[0081]

[0082] where q∈R n ,θ∈R n are the joint position vector and the motor position vector respectively, and n is the order of the system; is the joint acceleration vector; is the motor acceleration vector; M(q)∈R n×n is an unknown non-singular symmetric positive definite matrix representing joint inertia; J∈R n×n is an unknown non-singular symmetric positive definite matrix representing the mot...

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 relates to a finite time self-adaptive back stepping control method for a flexible joint mechanical arm based on a neural networks. The finite time self-adaptive back stepping control method for the flexible joint mechanical arm based on the neural networks is arranged with a design of self-adaptive back stepping control by utilizing the neural networks and a finite time control method, and overcomes the unknown uncertain problems of the flexible joint mechanical arm. According to the finite time self-adaptive back stepping control method, a system tracking error is converged toa field near a balance point within a limited time is realized through a self-adaptive finite-time virtual controller in each step of the back stepping control; and two simple neural networks are applied to approach and compensate an unknown item of a system, and a large amount of calculation amount in traditional back stepping control is reduced. The finite time self-adaptive back stepping control method has the advantages that the unknown uncertain item of the system can be compensated, the problem that the calculation amount is large in the traditional back stepping control is solved, the system tracking error can be converged within a limited time, and system finite time tracking is realized.

Description

technical field [0001] The invention relates to a finite time self-adaptive backstepping control method of a flexible joint manipulator based on a neural network, in particular to a control method of a flexible joint manipulator with unknown uncertain items. Background technique [0002] Due to its flexible movements, small motion inertia, high work efficiency, stability and reliability, the role of robotic arms in real life has become increasingly prominent, especially in high-precision fields, such as industrial design, aerospace, medical equipment, etc. . With the development of science and technology, people have higher and higher requirements for the precision of the manipulator, but the actual complex uncertain factors in the manipulator seriously affect the control performance of the manipulator and the limitation of the technical level. In order to achieve higher precision and performance requirements, the flexibility of the joints of the manipulator is considered, ...

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 Patents(China)
IPC IPC(8): B25J9/16B25J17/00
CPCB25J9/1605B25J9/161B25J9/1638B25J17/00
Inventor 陈强施卉辉孙明轩何熊熊庄华亮
Owner ZHEJIANG UNIV OF TECH
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

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap