Iterative learning control of Hamiltonian systems: I/O based optimal control approach
Iterative learning control of Hamiltonian systems: I/O based optimal control approach
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DOI：
10.1109/tac.2003.817908
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发表时间：
2003-10
期刊：
影响因子：
--
通讯作者：
K. Fujimoto;T. Sugie
中科院分区：
文献类型：
--
作者：
K. Fujimoto;T. Sugie
在本文中，提出了一种适用于机电系统的一类哈密顿控制系统的新颖迭代学习控制方案。所提出的方法具有以下显着特点。该方法不需要目标系统模型的精确知识或输出信号的时间导数。尽管缺乏信息，跟踪误差在L/sub 2/ sense 上单调减小，而且当其应用于机械系统时，可以实现完美的跟踪。本笔记中证明的哈密顿系统的自伴相关属性在这种学习控制中发挥着关键作用。这些属性对于一般最优控制也很有用。此外，机器人操纵器的实验证明了该方法的有效性。
In this note, a novel iterative learning control scheme for a class of Hamiltonian control systems is proposed, which is applicable to electromechanical systems. The proposed method has the following distinguished features. This method does not require either the precise knowledge of the model of the target system or the time derivatives of the output signals. Despite the lack of information, the tracking error monotonously decreases in L/sub 2/ sense and, further, perfect tracking is achieved when it is applied to mechanical systems. The self-adjoint related properties of Hamiltonian systems proven in this note play the key role in this learning control. Those properties are also useful for general optimal control. Furthermore, experiments of a robot manipulator demonstrate the effectiveness of the proposed method.