System identification method inheriting steady-state characteristics of existing model

Yoshimura, Sho; Matsubayashi, Ayaka; Inoue, Masakí

doi:10.1080/00207179.2018.1458158

Cited by 9 publications

(11 citation statements)

References 19 publications

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“…In formal methods literature, the model learning problems were studied under different names, like system identification [32], grammatical inference [33], regular inference [34], regular extrapolation [35], model learning [36], or active automata learning [37], etc. We do not distinguish these names, sometimes we use them interchangeably, and here we just use the term model learning or learning model instead.…”

Section: Taxonomy Of Learning Algorithms In Formal Methodsmentioning

confidence: 99%

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

et al. 2020

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Formal methods play an important role in testing and verifying software quality, especially in modern society with rapid technological updates. Learning-based techniques have been extensively applied to learn (a model or model-free) for formal verification and to learn system specifications, and resulted in numerous contributions. Due to the fact that adequate system models are often difficult to design manually and manual definition of specifications for such software systems gets infeasible, which motivate new research directions in learning models and/or specifications from observed system behaviors automatically. This paper mainly concentrates on learning-based techniques in formal methods area. An up-to-date overview of the current state-of-the-art in learning-based formal methods is provided in the paper. This paper is not a comprehensive survey of learning-based techniques in formal methods area, but rather as a survey of the taxonomy, applications and possible future directions in learning-based formal methods.

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Section: Taxonomy Of Learning Algorithms In Formal Methodsmentioning

confidence: 99%

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

et al. 2020

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“…Problem 2. Given a positive constant γ and data matrices (U k , Y k , Ψ k , Ψ k+1 ), we solve the following optimization problem: min P,A,B,C J(A, B, C) sub to (7), (8).…”

Section: Problem Settingmentioning

confidence: 99%

“…Noting that J(A, B, C) = J(P −1 M, P −1 N, C), the cost function is no more convex in decision variables (P, M, N, C). Therefore, the problem of minimizing J(P −1 M, P −1 N, C) subjected to (7) and ( 13) is still nonconvex. In the following, we address the approximation of the non-convex problem into a convex one.…”

Section: Convex Approximation Of Problemmentioning

confidence: 99%

“…Function J W (P, M, N, C) is convex in matrices (P, M, N, C). The minimization problem of J W (P, M, N, C) under inequalities (7) and ( 13) is in the class of the convex optimization. The optimization problem is summarized as follows.…”

Section: Convex Approximation Of Problemmentioning

confidence: 99%

“…To further improve the efficiency and reliability, there have been many trials on data-driven modeling while incorporating a priori known information on the dynamical system. Some examples of the a priori known information include the properties of linear dynamical systems such as stability [3], dominant eigen-mode [4,5], steady-state response [6,7], passivity [8,9], frequency response [10], system moment [11], etc. The general aim of this paper is to extend data-driven modeling techniques utilizing such a priori known information for linear systems to nonlinear system modeling.…”

Section: Introductionmentioning

confidence: 99%

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Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Hara

Inoue

2021

Mathematics

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In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

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Kernel-based identification with frequency domain side-information

Khosravi

Smith

2023

Automatica

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System identification method inheriting steady-state characteristics of existing model

Cited by 9 publications

References 19 publications

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Kernel-based identification with frequency domain side-information

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