Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems
Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems
复制标题
线性保守端口和自伴哈密顿系统的识别和数据驱动的降阶建模
DOI：
10.1109/cdc.2013.6759873
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Schaft
中科院分区：
文献类型：
--
作者：
P. Rapisarda;A. Schaft
给定一组足够多的向量指数轨迹的保守端口哈密顿系统和供应率，我们计算出一组相应的状态轨迹通过因式分解一个常数皮克样矩阵。然后通过求解涉及系统轨迹和计算的状态轨迹的线性方程组来获得状态方程。如果只对Pick矩阵的一个主子矩阵进行因式分解，则我们的过程产生通过全阶投影获得的低阶保守端口哈密顿模型。我们还描述了一个类似的方法来识别和模型降阶自伴哈密顿系统。
Given a sufficiently numerous set of vector-exponential trajectories of a conservative port-Hamiltonian system and the supply rate, we compute a corresponding set of state trajectories by factorizing a constant Pick-like matrix. State equations are then obtained by solving a system of linear equations involving the system trajectories and the computed state ones. If a factorization of only a principal submatrix of the Pick matrix is performed, our procedure yields a lower-order conservative port-Hamiltonian model obtained by projection of the full-order one. We also describe a similar approach to identification and model-order reduction for self-adjoint Hamiltonian systems.
影响因子：
6.8
作者：
Astolfi, Alessandro
通讯作者：
Astolfi, Alessandro
分部积分的状态图
影响因子：
2.2
作者：
Van Der Schaft A
通讯作者：
Van Der Schaft A