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离散系统迭代学习型瞬时最优控制及作动器位置优化研究

童少伟 唐怀平

童少伟, 唐怀平. 离散系统迭代学习型瞬时最优控制及作动器位置优化研究[J]. 应用数学和力学, 2016, 37(2): 160-172. doi: 10.3879/j.issn.1000-0887.2016.02.005
引用本文: 童少伟, 唐怀平. 离散系统迭代学习型瞬时最优控制及作动器位置优化研究[J]. 应用数学和力学, 2016, 37(2): 160-172. doi: 10.3879/j.issn.1000-0887.2016.02.005
TONG Shao-wei, TANG Huai-ping. Iterative Learning Instantaneous Optimal Control of Discrete Systems and Optimization[J]. Applied Mathematics and Mechanics, 2016, 37(2): 160-172. doi: 10.3879/j.issn.1000-0887.2016.02.005
Citation: TONG Shao-wei, TANG Huai-ping. Iterative Learning Instantaneous Optimal Control of Discrete Systems and Optimization[J]. Applied Mathematics and Mechanics, 2016, 37(2): 160-172. doi: 10.3879/j.issn.1000-0887.2016.02.005

离散系统迭代学习型瞬时最优控制及作动器位置优化研究

doi: 10.3879/j.issn.1000-0887.2016.02.005
基金项目: 国家自然科学基金(51378437)
详细信息
    作者简介:

    童少伟(1985—),男,博士生(通讯作者. E-mail: wt900800@126.com).

  • 中图分类号: P315.9

Iterative Learning Instantaneous Optimal Control of Discrete Systems and Optimization

Funds: The National Natural Science Foundation of China(51378437)
  • 摘要: 以线性离散系统为研究对象,以瞬时最优化控制和智能算法中的迭代学习控制为基础,以系统响应期望值与实际值之差为反馈信号,以离散系统的二次型性能泛函为目标函数,提出了迭代学习型瞬时最优控制算法.该方法以瞬时最优化控制算法初始化控制信号,并采用迭代学习控制在线实时修正控制信号以提高主动控制的效果.针对迭代学习型瞬时最优化控制算法迭代的特性,采用范数方法给出了该算法收敛的充分条件.数值算例表明,迭代学习型瞬时最优控制算法较离散瞬时最优控制算法有较明显的优势.同时,基于改进遗传算法,对主动控制器位置优化进行了讨论.数值分析结果表明:部分楼层设置主动控制器且安装位置经过优化后,其控制效果可接近甚至优于全楼层设置主动控制器时的控制效果.
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出版历程
  • 收稿日期:  2015-07-08
  • 修回日期:  2015-12-08
  • 刊出日期:  2016-02-15

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