Iterative learning control of Hamiltonian systems based on self-adjoint structure-I/O based optimal control
Iterative learning control of Hamiltonian systems based on self-adjoint structure-I/O based optimal control
复制标题
基于自伴结构的哈密顿系统迭代学习控制-基于I/O的最优控制
DOI：
10.1109/sice.2002.1195825
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
T. Sugie
中科院分区：
文献类型：
--
作者：
K. Fujimoto;T. Sugie
本文回顾了一种新颖的迭代学习方案，以实现物理系统的最优控制。证明一类哈密顿系统的变分系统具有自伴状态空间实现，即变分系统及其伴随系统具有相同的状态空间实现。这意味着给定哈密顿系统的变分系统的伴随的输入输出映射可以仅使用原始系统的输入输出映射来计算。该属性应用于具有最优控制类型成本函数的基于伴随的迭代学习控制。所提出的方法有望成为基于 I/O 的新型最优控制的基础。
This paper reviews a novel iterative learning scheme to achieve optimal control for physical systems. It is shown that the variational systems of a class of Hamiltonian systems have self-adjoint state-space realizations, that is, the variational system and its adjoint have the same state-space realizations. This implies that the input-output mapping of the adjoint of the variational system of a given Hamiltonian system can be calculated by only using the input-output mapping of the original system. This property is applied to adjoint based iterative learning control with optimal control type cost functions. The proposed method is expected to be a basis for new I/O based optimal control.