Data-Driven Simulation of Generalized Bilinear Systems via Linear Time-Invariant Embedding

被引：6
|
作者：
Markovsky, Ivan [1 ,2 ]
机构：
[1] Int Ctr Numer Methods Engn CIMNE, Gran Capitan, Barcelona 08034, Spain
[2] Catalan Inst Res & Adv Studies ICREA, Barcelona 08010, Spain
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2023年 / 68卷 / 02期
关键词：
Linear systems; Nonlinear systems; Mathematical models; Trajectory; Data models; Kernel; Difference equations; Behavioral approach; data-driven methods; nonlinear systems; system identification; ALGORITHMS;
D O I：
10.1109/TAC.2022.3146726
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Nonparameteric representations of linear time-invariant systems that use Hankel matrices constructed from data are the basis for data-driven simulation and control. This article extends the approach to data-driven simulation of a class of nonlinear systems, called generalized bilinear. The generalized bilinear class includes Hammerstein, finite-lag Volterra, and bilinear systems. The key step of the generalization is an embedding result that is of independent interest. The behavior of a nonlinear system is included into the behavior of a linear time-invariant system. The method proposed is illustrated and compared with a model-based method on simulation examples and real-life data.
引用
收藏
页码：1101 / 1106
页数：6
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