Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A self-adaptive neural network control method and device based on friction compensation

A neural network control and friction compensation technology, which is applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems that are not easy, and system analysis is difficult to achieve based on model compensation

Active Publication Date: 2020-09-18
中科南京移动通信与计算创新研究院
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the friction model is a highly nonlinear and complex model, and it is not easy to establish an accurate friction model, and even if a relatively complete friction model is obtained, it will be difficult to realize system analysis and model-based compensation due to its complex expressions

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 self-adaptive neural network control method and device based on friction compensation
  • A self-adaptive neural network control method and device based on friction compensation
  • A self-adaptive neural network control method and device based on friction compensation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0092] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0093] It should be noted that, if there is no conflict, various features in the embodiments of the present invention may be combined with each other, and all of them are within the protection scope of the present invention. In addition, although the functional modules are divided in the schematic diagram of the device, and the logical order is shown in the flowchart, in some cases, the division of modules in the device or the sequence shown in the flowchart can be performed in different ways. or the steps described.

[0094] Currently, many techniques have been studied to address the effects of f...

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 friction-compensation-based adaptive neural network control method and device. The method comprises: establishing a motor position servo system model; designing an adaptive neural network controller based on friction compensation; according to the designed friction-compensation-based adaptive neural network controller, stability identification is carried out on the motorservo system based on a Lyapunov stability theory; and with the Barbalat lemma, a global asymptotic stability result of the system is obtained. According to the invention, modeling of the friction characteristics in the motor servo system is carried out by using a continuous friction model and the friction characteristics and other nonlinear disturbances in the motor servo system are compensated well by combining the self-learning ability of the neural network and the estimation ability of the parameter estimator, so that the system's stable tracking accuracy is improved substantially.

Description

technical field [0001] The invention relates to the field of motor servo system control, in particular to an adaptive neural network control method and device based on friction compensation. Background technique [0002] Friction is a complex phenomenon that exists in the relative motion of all mechanical structures, and it depends on the physical properties of the contact surfaces, relative speed and lubrication conditions, etc. In practical applications, friction not only seriously affects the control accuracy of the servo system, but also causes undesired stick-slip motion or limit cycle oscillation to occur. [0003] Currently, many techniques have been studied to address the effects of friction in motor servo systems. For example, PID control has a simple structure, does not depend on the mathematical model of the system, and has strong engineering practicability, but its control effect on nonlinear friction and strong disturbance systems is not good. With the continu...

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): G05B13/04
Inventor 刘雷石晶林胡金龙
Owner 中科南京移动通信与计算创新研究院
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
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