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

Multi-agent cooperative control system and method for process industry

A process industry and control method technology, applied in general control systems, control/regulation systems, program control, etc., can solve problems such as obstacles to direct use, reduce collection and processing steps, strong robustness, and realize real-time dynamic optimization Effect

Inactive Publication Date: 2019-11-08
QILU UNIV OF TECH
View PDF6 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This poses challenges for training stability and prevents direct utilization of prior experience

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
  • Multi-agent cooperative control system and method for process industry
  • Multi-agent cooperative control system and method for process industry
  • Multi-agent cooperative control system and method for process industry

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] In the technical solutions disclosed in one or more embodiments, such as figure 1 As shown, a multi-agent cooperative control system for the process industry, including the system agent (SystemAgent) and agents in each production stage, each production stage is set with multiple agents that can communicate with each other and cooperate with each other, so The above-mentioned system agents and agents in each production stage are set in layers. The system agents are set on the upper layer for unified scheduling and task assignment of agents in each production stage; the agents in each production stage are set in the lower layer.

[0036] For example, the agent can be a robot in an unmanned production workshop. The multi-agent collaborative control system proposed in this embodiment is an intelligent collaborative control system oriented to the whole process, such as figure 1 with 2 As shown, it is composed of system Agent and Agent of each production stage. Each product...

Embodiment 2

[0074] This embodiment provides a multi-agent cooperative control system for the process industry, including the following modules:

[0075] Model network building module: used to establish a neural network model from state value to observed value based on the real state value s and real observed value o of the experience pool; based on the max-min depth deterministic policy gradient algorithm, establish the actor network that outputs the initial policy and the critic network for judging feedback;

[0076] Observation value prediction module: used to collect the current state value s of the current agent i t , the current state value s t Input to the trained neural network model to obtain the current state value s t Corresponding to predict the observed value of the current agent

[0077] Initial policy output module: used to convert the current state value s of the current agent t and predict the observations of the current agent Input the actor network, and get the a...

Embodiment 3

[0080] This embodiment provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are executed by the processor, the steps described in Embodiment 1 are completed.

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 provides a multi-agent cooperative control system and method for the process industry. A neutral network model from state values to observation values is arranged for feature extraction,important features of data are better extracted, and processing of mass data or even high-dimensional data can be adapted to. Then the minimax deep deterministic policy gradient algorithm is adoptedfor learning, wherein actor and critic networks in the minimax deep deterministic policy gradient algorithm conduct learning from a new neural network instead of conducting learning from initial observation. By means of the method for conducting state representation learning by utilizing the neutral network, the network can well capture features, and the adaptability to data is higher.

Description

technical field [0001] The present disclosure relates to the technical field related to intelligent control, and specifically relates to a multi-agent cooperative control system and method for the process industry. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] The process industry involves many industries including chemistry, oil refining, metallurgy and pharmaceuticals, and it plays an important role in China's industrial production. As a leading industry in the country, it plays a pivotal role in the development of the national economy. However, the characteristics of high complexity, strong nonlinearity, strong correlation, and uncertainty in the process industry make it difficult to realize optimization problems in theoretical research and practical applications. In order to realize the transformation of globalized production, ma...

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): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 姜雪松胡大鹏朱庆存孟超
Owner QILU 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