基于深度学习的自适应控制算法在工业机器人中的应用研究  

Research on application of adaptive control algorithm based on deep learning in industrial robot

作　　者：朱士林 吴中华 庄子游 刘晓倩 ZHU Shilin;WU Zhonghua;ZHUANG Ziyou;LIU Xiaoqian(Jinan Wolfis Technology Co.,Ltd.,Jinan 250119,China)

机构地区：[1]济南沃尔菲斯科技有限公司,济南250119

出　　处：《计算机应用文摘》2025年第2期88-90,共3页

摘　　要：随着工业自动化技术的迅速发展,人们对工业机器人控制系统的需求日益增加。传统控制系统存在环境适应性和灵活性不足等问题。基于此,文章提出了一种基于深度学习的自适应控制算法,并在工业机器人上进行了应用。该算法通过结合卷积神经网络(CNN)和强化学习(RL),使得机器人能够实时学习并适应动态变化的操作环境。实验结果显示,相较于传统算法,该算法在任务执行效率和适应性方面有显著提升。With the rapid development of industrial automation technology,the demand for industrial robot control system is increasing day by day.The traditional control system has some problems such as insufficient environmental adaptability and flexibility.Based on this,an adaptive control algorithm based on deep learning is proposed and applied to industrial robots.By combining convolutional neural networks(CNN)and reinforcement learning(RL),the algorithm enables the robot to learn in real time and adapt to the dynamically changing operating environment.Experimental results show that compared with traditional algorithms,the proposed algorithm has a significant improvement in task execution efficiency and adaptability.

关 键 词：深度学习 自适应控制 工业机器人 卷积神经网络 强化学习 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]