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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

刘德荣, 李宏亮, 王鼎. 基于数据的自学习优化控制:研究进展与展望. 自动化学报, 2013, 39(11): 1858-1870. doi: 10.3724/SP.J.1004.2013.01858
引用本文: 刘德荣, 李宏亮, 王鼎. 基于数据的自学习优化控制:研究进展与展望. 自动化学报, 2013, 39(11): 1858-1870. doi: 10.3724/SP.J.1004.2013.01858
LIU De-Rong, LI Hong-Liang, WANG Ding. Data-based Self-learning Optimal Control: Research Progress and Prospects. ACTA AUTOMATICA SINICA, 2013, 39(11): 1858-1870. doi: 10.3724/SP.J.1004.2013.01858
Citation: LIU De-Rong, LI Hong-Liang, WANG Ding. Data-based Self-learning Optimal Control: Research Progress and Prospects. ACTA AUTOMATICA SINICA, 2013, 39(11): 1858-1870. 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
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

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

Funds: 

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

  • 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.
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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

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