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

A control method for manipulator with fixed-time predetermined performance recurrent neural network

A cyclic neural network and control method technology, which is applied in the field of fixed-time predetermined performance cyclic neural network manipulator control, can solve problems such as failure to consider transient and steady-state performance, inability to ensure tracking error convergence, and simplify controller design. , overcome the singularity problem, reduce the effect of tracking time

Active Publication Date: 2022-03-11
NORTHWESTERN POLYTECHNICAL UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these control methods fail to consider transient and steady-state performance, and cannot guarantee that the tracking error converges along a pre-specified performance function

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 control method for manipulator with fixed-time predetermined performance recurrent neural network
  • A control method for manipulator with fixed-time predetermined performance recurrent neural network
  • A control method for manipulator with fixed-time predetermined performance recurrent neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0270] Example: Robotic Arm Driven by a DC Motor

[0271] Taking the manipulator driven by DC motor as an example to illustrate the effectiveness of the above fixed time predetermined performance cyclic neural network control method in realizing the ideal track of the driven manipulator. The mathematical model of the manipulator composed of the mechanical subsystem (1) and the electrical subsystem (2) can be expressed as:

[0272]

[0273] The system parameters are selected as L=0.05,K B =0.5, R=0.5, ΔI=0.1cos(t). The dead zone model can be written as:

[0274]

[0275] A neural network control method for a fixed time predetermined performance of a DC motor-driven mechanical arm of the present embodiment includes the following steps:

[0276] (1) Determine the control target: the reference output signal is selected as The predetermined performance function is chosen as The control target is determined as the system output can track the reference output of the sy...

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 control method of a cyclic neural network manipulator with predetermined performance at fixed time, comprising: (1) establishing a mathematical model of a DC motor-driven manipulator, and establishing an actuator model with an unknown nonlinear dead zone; (2) system reference Output, design the performance function that the tracking error needs to satisfy; (3) Design a fixed-time predetermined performance recurrent neural network controller, neural network weight update law and fixed-time differentiator, so that the system output can track the upper reference output trajectory within a fixed time , while limiting the system tracking error within the pre-specified performance boundary; (4) Carry out stability analysis on the control system, and determine the controller parameters according to the stability analysis results. The method proposed by the invention can realize fixed-time predetermined performance trajectory tracking, thereby reducing the tracking time, improving control accuracy, and ensuring the transient and steady-state performance of the system in the control process.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a method for controlling a mechanical arm of a cyclic neural network with predetermined performance at fixed time. Background technique [0002] High-performance motion control is critical to many industrial applications. High-performance motion control requires the motor to be able to drive the load (mechanical arm) to move along a predetermined trajectory, which puts forward high requirements on tracking time, tracking accuracy, and system transient and steady-state tracking performance. Traditional control methods are based on feed-forward neural networks. However, feed-forward neural network is a static mapping and cannot express dynamic mapping without delay. In addition, the function approximation performance of the feedforward neural network is sensitive to the training data, and its function approximation performance will deteriorate when its input is greatly perturbed...

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
CPCG05B13/042
Inventor 倪骏康
Owner NORTHWESTERN POLYTECHNICAL UNIV
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