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基于确定学习的机器人任务空间自适应神经网络控制

吴玉香 王聪

吴玉香, 王聪. 基于确定学习的机器人任务空间自适应神经网络控制. 自动化学报, 2013, 39(6): 806-815. doi: 10.3724/SP.J.1004.2013.00806
引用本文: 吴玉香, 王聪. 基于确定学习的机器人任务空间自适应神经网络控制. 自动化学报, 2013, 39(6): 806-815. doi: 10.3724/SP.J.1004.2013.00806
WU Yu-Xiang, WANG Cong. Deterministic Learning Based Adaptive Network Control of Robot in Task Space. ACTA AUTOMATICA SINICA, 2013, 39(6): 806-815. doi: 10.3724/SP.J.1004.2013.00806
Citation: WU Yu-Xiang, WANG Cong. Deterministic Learning Based Adaptive Network Control of Robot in Task Space. ACTA AUTOMATICA SINICA, 2013, 39(6): 806-815. doi: 10.3724/SP.J.1004.2013.00806

基于确定学习的机器人任务空间自适应神经网络控制

doi: 10.3724/SP.J.1004.2013.00806
基金项目: 

国家自然科学基金(60934001, 61225014, 61075082)资助

详细信息
    通讯作者:

    吴玉香

Deterministic Learning Based Adaptive Network Control of Robot in Task Space

Funds: 

Supported by National Natural Science Foundation of China(60934001, 61225014, 61075082)

  • 摘要: 针对产生回归轨迹的连续非线性动态系统, 确定学习可实现未知闭环系统动态的局部准确逼近. 基于确定学习理论, 本文使用径向基函数(Radial basis function, RBF)神经网络为机器人任务空间跟踪控制设计了一种新的自适应神经网络控制算法, 不仅实现了闭环系统所有信号的最终一致有界, 而且在稳定的控制过程中, 沿着回归跟踪轨迹实现了部分神经网络权值收敛到最优值以及未知闭环系统动态的局部准确逼近. 学过的知识以时不变且空间分布的方式表达、以常值神经网络权值的方式存储, 可以用来改进系统的控制性能, 也可以应用到后续相同或相似的控制任务中, 节约时间和能量. 最后, 用仿真说明了所设计控制算法的正确性和有效性.
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出版历程
  • 收稿日期:  2011-12-16
  • 修回日期:  2012-10-25
  • 刊出日期:  2013-06-20

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