基于循环神经网络的工业机器人能耗预测方法研究

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关键词：工业机器人；能耗预测；神经网络中图分类号：U414 文献标志码：ADOI:10.19968/j.cnki.hnkj.1003-5168.2025.14.005文章编号:1003-5168(2025)14-0028-04

Research on Energy Consumption Prediction Method for Industrial RobotsBased on RecurrentNeural Network

YANG Xue (Chongqing Polytechnic University of Electronic Technology, Chongqing 401331, China)

Abstract: [Purposes] To address the issues of high energy consumption and low energy efficiency in industrial robots and achieve energy consumption optimization,an energy consumption prediction model for industrial robots based on a recurrent neural network integrating Long Short-Term Memory (LSTM) and Attention mechanism is proposed.[Methods] First，based on the analysis of energy consumption sources including motor iron loss,copper loss,and joint friction,the total power model and total energy consumption formula of industrial robots were established. Second，the parameters of the LSTMAttention model were configured,with Mean Absolute Error( MAE )，Mean Absolute Percentage Error (MAPE),and Root Mean Square Error (RMSE) employed as evaluation metrics.Finally,the operation data of the robot were collcted,and experiments were conducted using 5-fold cross-validation.[Findings] The experimental results showed that the RMSE was 989.52, the MAE was725.15,andtheMAPE was 6.161% . The relative error of the model's prediction results was low,indicating that it could ffectively predict the energy consumption of industrial robots.[Conclusions] The LSTM-Attention model

收稿日期：2025-04-07

demonstrates significant advantages in energy consumption prediction, which can effectively reduce the prediction errors and improve the prediction accuracy,thus providing substantial support for subsequent energy consumption research.

Keywords: industrial robots; energy consumption optimization; neural network

0 引言

工业机器人作为制造系统中的关键装备，在现代制造业中发挥着重要作用。（剩余6778字）