Multi-agent cooperative control system and method for process industry

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...

Images