基于数据的自学习优化控制：研究进展与展望

刘德荣; 李宏亮; 王鼎

doi:10.3724/SP.J.1004.2013.01858

基于数据的自学习优化控制：研究进展与展望

doi: 10.3724/SP.J.1004.2013.01858

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

基金项目:

国家自然科学基金(61034002,61233001,61273140)资助

详细信息

作者简介:
刘德荣中国科学院自动化研究所研究员. 主要研究方向为智能控制理论及应用, 自适应动态规划, 人工神经网络, 计算神经科学, 电力系统运行与控制. E-mail: derong.liu@ia.ac.cn

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- 文章访问数: 3327
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- 被引次数: 0
出版历程
- 收稿日期: 2013-06-28
- 修回日期: 2013-09-02
- 刊出日期: 2013-11-20

Data-based Self-learning Optimal Control： Research Progress and Prospects

1.
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds:

Supported by National Natural Science Foundation of China (61034002, 61233001, 61273140)

摘要

摘要: 自适应动态规划(Adaptive dynamic programming, ADP)方法可以解决传统动态规划中的"维数灾"问题, 已经成为控制理论和计算智能领域最新的研究热点. ADP方法采用函数近似结构来估计系统性能指标函数, 然后依据最优性原理来获得近优的控制策略. ADP是一种具有学习和优化能力的智能控制方法, 在求解复杂非线性系统的最优控制问题中具有极大的潜力. 本文对ADP的理论研究、算法实现、相关应用等方面进行了全面的梳理, 涵盖了最新的研究进展, 并对ADP的未来发展趋势进行了分析和展望.
- 自适应动态规划 /
- 近似动态规划 /
- 强化学习 /
- 神经网络 /
- 智能控制
Abstract: Adaptive dynamic programming (ADP) method can solve the problem of "curse of dimensionality" in the traditional dynamic programming, and has recently become a hot topic in the field of control theory and computational intelligence. For ADP method, a function approximation structure is used to estimate the performance index function, and then the approximate optimal control policy can be obtained based on the principle of optimality. As a kind of intelligent control methods with learning and optimization capabilities, ADP has great potential in solving the optimal control problem of complex nonlinear systems. This paper presents a comprehensive survey on the theoretical research, algorithm development, and related applications of ADP, which covers the latest research progress. It also analyzes and predicts the future development trend of ADP.
- Adaptive dynamic programming (ADP) /
- approximate dynamic programming /
- reinforcement learning /
- neuralnetworks /
- intelligent control

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