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

Diagonal recurrent neural network control strategy based on Q learning algorithm

A recursive neural network and learning algorithm technology, applied in the field of diagonal recursive neural network control strategy, can solve the problem of PID neural network initialization for a long time, to improve dynamic characteristics and robustness, speed up iteration speed, and enhance anti-interference ability Effect

Active Publication Date: 2020-09-15
CHANGCHUN UNIV OF TECH
View PDF6 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that it takes a long time for PSO to initialize the PID neural network.

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
  • Diagonal recurrent neural network control strategy based on Q learning algorithm
  • Diagonal recurrent neural network control strategy based on Q learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be noted that the technical features and combinations of technical features described in the following embodiments should not be regarded as isolated, and they can be combined with each other to achieve better technical effects.

[0052] Such as figure 1 As shown, a kind of diagonal recursive neural network control strategy based on the Q learning algorithm proposed by the present invention, the specific architecture includes a Q learning algorithm optimized DRNN module and a brushless DC motor, and the specific control method is as follows: sampling obtains the brushless DC motor input Speed ​​Y d (k) and the output speed y(k), calculate the speed error e(k)=Y d (k)-y(k), according to the speed error e(k), for e(k), e(k)-e(k-1), e(k)-2e(k-1)+e(k -2) Perform normalization processing as the input x of Q-DRNN 1 ,x 2 ,x 3 . At this ...

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 designs a diagonal recurrent neural network (DRNN) control strategy (Q-DRNN) based on a Q learning algorithm. The Q-DRNN organically combines the strong search capability of Q learning with the advantages of the built-in recursive ring structure, the dynamic mapping capability, the adaptive time-varying characteristic and the like of the DRNN, and is used for improving the working stability of a brushless direct current motor (BLDCM). In the Q-DRNN, the DRNN iterates an output variable through a unique recursive ring in a hidden layer, and optimizes the key weight of the output variable so as to accelerate the iteration speed; and meanwhile, the improved Q learning is introduced to correct the weight term factor of the DRNN, so that the DRNN has self-learning and online correction capabilities, the anti-interference capability and robustness of the system are enhanced, and the brushless direct current motor achieves a better control effect.

Description

technical field [0001] The invention belongs to the field of brushless direct current motor control methods, and in particular relates to a diagonal recursive neural network control strategy based on a Q learning algorithm. Background technique [0002] Due to its simple structure, large output and high efficiency, brushless DC motors have been widely used in the fields of national defense, aerospace, robotics, industrial process control, precision machine tools, automotive electronics, household appliances and office automation. The brushless DC motor plays an important role in the modern motor speed control system. Therefore, it is of great practical significance and application prospect to study the speed control strategy of the brushless DC motor with fast response speed, strong adjustment ability and high control precision. [0003] PID control is one of the earliest linear control strategies and has a long history. It remains the most commonly used control algorithm i...

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
CPCG05B13/04
Inventor 王宏志王婷婷胡黄水韩优佳
Owner CHANGCHUN UNIV OF TECH
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