深度学习在控制领域的研究现状与展望

段艳杰; 吕宜生; 张杰; 赵学亮; 王飞跃

doi:10.16383/j.aas.2016.c160019

深度学习在控制领域的研究现状与展望

doi: 10.16383/j.aas.2016.c160019

段艳杰^1,,
吕宜生^1,,
张杰^1,2,,
赵学亮^1,,
王飞跃^1, ,

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
2.
青岛智能产业技术研究院山东 266000

基金项目:

国家自然科学基金 71402178

国家自然科学基金 71232006

国家自然科学基金 61233001

详细信息

作者简介:
段艳杰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博士研究生.主要研究方向为智能交通系统,机器学习及应用.E-mail:duanyanjie2012@ia.ac.cn

吕宜生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助理研究员.主要研究方向为交通数据分析,动态交通建模,平行交通管理与控制系统.E-mail:yisheng.lv@ia.ac.cn

张杰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助理研究员.主要研究方向为拍卖机制,最优控制与博弈论.E-mail:jie.zhang@ia.ac.cn

赵学亮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博士研究生,中国自动化学会工程师.主要研究方向为社会计算,智能信息处理.E-mail:xueliang.zhao@ia.ac.cn

通讯作者:
王飞跃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研究员.主要研究方向为智能系统和复杂系统的建模,分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn

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出版历程
- 收稿日期: 2015-12-26
- 录用日期: 2016-03-26
- 刊出日期: 2016-05-01

Deep Learning for Control: The State of the Art and Prospects

DUAN Yan-Jie^1
,,
LV Yi-Sheng^1
,,
ZHANG Jie^{1,2
,},
ZHAO Xue-Liang^1
,,
WANG Fei-Yue^{1
, ,}

1.
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
Qingdao Academy of Intelligent Industries, Shandong 266000

Funds:

Supported by National Natural Science Foundation of China 71402178

Supported by National Natural Science Foundation of China 71232006

Supported by National Natural Science Foundation of China 61233001

More Information

Author Bio:
Ph. D. candidate at The State Key Laboratory of Man- agement and Control for Complex Sys- tems, Institute of Automation, Chinese Academy of Sci- ences. Her research interest covers intelligent transporta- tion systems, machine learning and its application

Assistant professor at The State Key Laboratory of Man- agement and Control for Complex Sys- tems, Institute of Automation, Chinese Academy of Sci- ences. His research interest covers tra±c data analysis, dynamic tra±c modeling, and parallel tra±c management and control systems

Assistant professor at The State Key Laboratory of Manage- ment and Control for Complex Sys- tems, Institute of Automation, Chinese Academy of Sci- ences. His research interest covers online auctions, optimal control and game theory

g Ph. D. candi- date at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sci- ences, engineer at Chinese Association of Automation. His research interest covers social computing and intelligent in- formation processing

Corresponding author: WANG Fei-Yue Professor at The State Key Laboratory of Management and Control for Complex Systems, In- stitute of Automation, Chinese Academy of Sciences. His research interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper

摘要

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.
- 深度学习 /
- 控制 /
- 特征 /
- 自适应动态规划
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.
- Deep learning /
- control /
- feature /
- adaptive dynamic programming (ADP)

HTML全文

图 1 DBN网络结构

Fig. 1 The structure of DBN

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图 2 SAE网络结构

Fig. 2 The structure of SAE

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图 3 CNN网络结构^[6]

Fig. 3 quad The structure of CNN^[6]

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图 4 RNN网络结构

Fig. 4 The structure of RNN

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图 5 深度学习在控制系统各环节的应用

Fig. 5 The application of deep learning in control system

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图 6 机械手抓取系统^[14]

Fig. 6 Robotic grasping system^[14]

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图 7 使用深度学习进行Atari游戏

Fig. 7 Playing Atari with deep learning

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图 8 进行状态预测和学习 $Q$ 函数的深度网络^[20]

Fig. 8 Neural network for learning state prediction and $Q$ function^[20]

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图 9 进行运动控制函数研究的深度网络

Fig. 9 Deep neural network for motor control function

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图 10 深度模糊控制网络

Fig. 10 Neuro-fuzzy network

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图 11 自适应动态规划的神经网络结构

Fig. 11 The network structure of adaptive dynamic programming

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图 12 平行控制系统^[50]

Fig. 12 Parallel control systems^[50]

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