Computationally efficient identification of continuous-time Lur’e-type systems with stability guarantees

Shakib, Mohammad Fahim; Pogromsky, Alexander Yu.; Pavlov, Alexey; Wouw, Nathan van de

doi:10.1016/j.automatica.2021.110012

Cited by 14 publications

(1 citation statement)

References 51 publications

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“…High-accuracy and novel identi cation algorithm is increasingly popular due to the basis of the adaptive control. In a physical control system, the nonlinear system model based on sensor and data acquisition technology can better represent the characteristics of the actual system compared with a linear model [1][2][3]. us, nonlinear system identi cation has become a hot research subject in the automatic control community.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Zhang

et al. 2022

Mathematical Problems in Engineering

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In the process of actual system modeling, many systems exhibit nonlinear characteristics with memory. Thus, the parameter identification problem of the nonlinear system with memory usually appears in the system modeling. This report focuses on the nonlinear system identification of Wiener–Hammerstein-like model with memory hysteresis, in which a new recursive estimation way is introduced. In this algorithm, the estimation bias problem can be improved by introducing a data filtering technique. On the basis of the filtered data, some auxiliary matrices and vectors are proposed. Following this, the identification error variable is introduced by using auxiliary matrices and vectors with an adaptive forgetting factor. Afterward, the identification error variable is integrated into the design of parameter estimation adaptive law with recursive gain structure. By comparison with the classic estimation methods, the proposed algorithm shows an alternative identification algorithm design angle. In addition, it is strictly proved that the parameter estimation error converges to zero under a general excitation condition. Based on the results of indices M S E , compared with the existing methods, the performance improvements of the proposed method are 33.9 %, 41.26%, and 53.5%, respectively. In terms of indices P E M , the augmented performances of the developed scheme are 50%, 56.2%, and 68.4%, respectively, in comparison to the available schemes.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Zhang

et al. 2022

Mathematical Problems in Engineering

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Sampled Measurement-Based Robust Impulsive Filtering for Delayed Lur’e Implicit Hybrid Systems and Its Application in RC Circuit

Wang

Zhuang

Xia

et al. 2023

Circuits Syst Signal Process

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Uncertainty quantification in real-time parameter updating for digital twins using Bayesian inverse mapping models

Kessels,

Fey,

van de Wouw

2024

Nonlinear Dyn

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To achieve its full predictive potential, a digital twin must consistently and accurately reflect its physical counterpart throughout its operational lifetime.To this end, the inverse mapping parameter updating method enables physically interpretable parameter values to be updated, in real-time, for a wide range of (nonlinear) dynamical models using features extracted from measured response data. This paper proposes to extend this method by employing a probabilistic Bayesian neural network, which is trained offline using simulated data, to infer, again in real-time, probability distributions for the updating parameter values instead of (traditionally obtained) point estimates. As a result, the user obtains a quantification of the (un)certainty, providing insight into the degree of trust to be placed in the updated parameter values, which supports the decision-making process for which the digital twin is used. Additionally, it is proposed to include so-called ‘input parameters’ (that characterize the specific settings on the physical setup) as inputs to the neural network to allow for a broader applicability of the updating method. To validate the proposed methodology, it is applied, using both simulated and real-world measurements, to a medical mechanical ventilation system, in which information about uncertainty in the inferred parameter values is important. Parameter values of this system and their uncertainties are shown to be inferred with sufficient accuracy.

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Computationally efficient identification of continuous-time Lur’e-type systems with stability guarantees

Cited by 14 publications

References 51 publications

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Sampled Measurement-Based Robust Impulsive Filtering for Delayed Lur’e Implicit Hybrid Systems and Its Application in RC Circuit

Uncertainty quantification in real-time parameter updating for digital twins using Bayesian inverse mapping models

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