Data-Driven Identification of Dissipative Linear Models for Nonlinear Systems

• 成果类型: Article
• 署名作者: Sivaranjani, S.; Agarwal, Etika; Gupta, Vijay
• 署名单位: Purdue University System; Purdue University; University of Notre Dame; Purdue University System; Purdue University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3180810
• 发表日期: 2022
• 页码: 4978-4985
• 关键词: Perturbation methods; Integrated circuit modeling; Stability criteria; data models; System identification; Circuit stability; STANDARDS; Dissipativity; identification; learning; Nonlinear systems; Passivity

摘要

We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time-domain input-output data. We first learn an approximate linear model of the nonlinear system using standard system identification techniques and then perturb the system matrices of the linear model to enforce dissipativity, while closely approximating the dynamical behavior of the nonlinear system. Further, we provide an analytical relationship between the size of the perturbation and the radius in which the dissipativity of the linear model guarantees local dissipativity of the unknown nonlinear system. We demonstrate the application of this identification technique through two examples.