| 引用本文: | 郭文涛,梅生伟,刘锋.一种新型附加学习控制器及电力系统应用实例[J].控制理论与应用,2014,31(12):1723~1730.[点击复制] |
| GUO Wen-tao,MEI Sheng-wei,LIU Feng.A novel supplementary learning controller and its application in power systems[J].Control Theory and Technology,2014,31(12):1723~1730.[点击复制] |
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| 一种新型附加学习控制器及电力系统应用实例 |
| A novel supplementary learning controller and its application in power systems |
| 摘要点击 2369 全文点击 1752 投稿时间：2014-11-04 修订日期：2014-12-11 |
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| DOI编号 10.7641/CTA.2014.41029 |
| 2014,31(12):1723-1730 |
| 中文关键词 附加学习控制 在线 优化 自适应 近似动态规划 |
| 英文关键词 supplementary learning control online optimization adaptation approximate dynamic programming |
| 基金项目 国家自然科学基金资助项目(51377092); 国家重点基础研究专项经费资助项目(2012CB215103); 国家自然科学基金创新研究群体科学 基金资助项目(51321005). |
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| 中文摘要 |
| 维纳的《控制论》和钱学森的《工程控制论》共同奠定了经典控制理论的基础. 在此基础之上, 现代控制理论在性能优化和处理不确定性等方面对经典控制理论作了进一步的发展. 本文提出一种融合经典控制与现代控制的控制方法, 将属于现代控制的近似动态规划学习控制器以并联的方式附加到经典控制器上, 从而形成一类新型附加学习控制器. 该控制器采用基于策略迭代近似动态规划的训练算法和基于最小二乘的代价函数逼近算法, 从而具有高策略搜索效率和高样本利用效率, 其中动作依赖代价函数的引入使得在线学习不依赖系统模型. 总之, 所提附加学习控制器一方面融合了已有经典控制器的先验知识, 另一方面为已有经典控制器提供了可设计的目标函数和自趋优机制. 论文进一步从理论上严格证明了所提附加学习控制方法的稳定性和收敛性. 针对双馈风电场暂态无功控制问题的仿真研究验证了所提附加学习控制器的正确性和方法的有效性. |
| 英文摘要 |
| “Cybernetics” by Norbert Wiener and “Engineering Cybernetics” by Tsien Hsue-shen have laid a solid foundation for classical control theory. Based on the classical control theory, modern control theory makes advance in optimizing performance and handling uncertainties. In this paper, a supplementary learning controller is proposed as a method to incorporate the classical control theory and the modern control theory, which adds a supplementary learning controller based on approximate dynamic programming on an existing classical controller. Policy iteration approximated dynamic programming algorithm and least squares method are employed as the training algorithm, which enjoys the policysearch efficiency of policy iteration and the data-utilization efficiency of least squares. Action dependent cost function is introduced to make the online learning model-free. By using such a supplementary learning controller, the prior knowledge of the existing classical controller can be fully utilized. On the other hand, the supplementary learning controller can optimize the performance of the closed-loop system. Furthermore, stability and convergence of the proposed method is proved rigorously. Simulation studies on reactive power control of doubly-fed induction generators (DFIGs) based wind farm validate the proposed supplementary learning controller. |
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