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

页码：1101 / 1106

页数：6

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