| 深度学习在控制领域的研究现状与展望 |
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| 引用本文： | 段艳杰,吕宜生,张杰,赵学亮,王飞跃.深度学习在控制领域的研究现状与展望[J].自动化学报,2016,42(5):643-654. |
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| 作者姓名： | 段艳杰 吕宜生 张杰 赵学亮 王飞跃 |
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| 作者单位： | 1.中国科学院自动化研究所复杂系统管理与控制国家重点实验室 北京 100190 |
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| 基金项目： | 国家自然科学基金(71232006,61233001,71402178)资助 |
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| 摘 要： | 深度学习在特征提取与模型拟合方面显示了其潜力和优势. 对于存在高维数据的控制系统, 引入深度学习具有一定的意义. 近年来, 已有一些研究关注深度学习在控制领域的应用. 本文介绍了深度学习在控制领域的研究方向和现状, 包括控制目标识别、状态特征提取、系统参数辨识和控制策略计算. 并对相关的深度控制以及自适应动态规划与平行控制的方法和思想进行了描述. 总结了深度学习在控制领域研究中的主要作用和存在的问题, 展望了未来值得研究的方向.
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| 关 键 词： | 深度学习 控制 特征 自适应动态规划 |
| 收稿时间： | 2015-12-26 |
Deep Learning for Control: The State of the Art and Prospects |
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| DUAN Yan-Jie,LV Yi-Sheng,ZHANG Jie,ZHAO Xue-Liang,WANG Fei-Yue.Deep Learning for Control: The State of the Art and Prospects[J].Acta Automatica Sinica,2016,42(5):643-654. |
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| Authors: | DUAN Yan-Jie LV Yi-Sheng ZHANG Jie ZHAO Xue-Liang WANG Fei-Yue |
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| Affiliation: | 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 1001902.Qingdao Academy of Intelligent Industries, Shandong 266000 |
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| Abstract: | Deep learning has shown great potential and advantage in feature extraction and model fitting. It is significant to use deep learning for control problems involving high dimension data. Currently, there have been some investigations focusing on deep learning in control. This paper is a review of related work including control object recognition, state feature extraction, system parameter identification and control strategy calculation. Besides, this paper describes the approaches and ideas of deep control, adaptive dynamic programming and parallel control related to deep learning in control. Also, this paper summarizes the main functions and existing problems of deep learning in control, presents some prospects of future work. |
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| Keywords: | Deep learning control feature adaptive dynamic programming (ADP) |
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