Multi-agent collaborative control parameter prediction for intelligent precision loading

Abstract

Due to the low adjustment accuracy of manual prediction, conventional programmable logic controller systems can easily lead to inaccurate and unpredictable load problems. The existing multi-agent systems based on various deep learning models has weak ability for advanced multi-parameter prediction while mainly focusing on the underlying communication consensus. To solve this problem, we propose a hybrid model based on a temporal convolutional network with the feature crossover method and light gradient boosting decision trees (called TCN-LightGBDT). First, we select the initial dataset according to the loading parameters' tolerance range and supply supplementing method for the deviated data. Second, we use the temporal convolutional network to extract the hidden data features in virtual loading areas. Further, a two-dimensional feature matrix is reconstructed through the feature crossover method. Third, we combine these features with basic historical features as the input of the light gradient boosting decision trees to predict the adjustment values of different combinations. Finaly, we compare the proposed model with other related deep learning models, and the experimental results show that our model can accurately predict parameter values.