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

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

doi:10.1016/j.automatica.2021.110012

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

Mohammad Fahim Shakib (Corresponding author)
, Alexander Yu Pogromsky
, Alexey Pavlov
, Nathan van de Wouw

Research output: Contribution to journal › Article › Academic › peer-review

24 Citations (Scopus)

187 Downloads (Pure)

Abstract

In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment.

Original language	English
Article number	110012
Number of pages	14
Journal	Automatica
Volume	136
DOIs	https://doi.org/10.1016/j.automatica.2021.110012
Publication status	Published - Feb 2022

Bibliographical note

Funding Information:
The authors would like to thank DEMCON Advanced Mechatronics, Best, The Netherlands, for the availability of the mechanical ventilation setup for the experimental study. Moreover, the authors would like to thank Prof. Dr. Ir. Johan Schoukens and Dr. Ir. Maarten Schoukens from the Eindhoven University of Technology, Eindhoven, The Netherlands, for the valuable and fruitful discussions. Furthermore, the authors would like to thank Niels Vervaet from the Eindhoven University of Technology, Eindhoven, The Netherlands, for his contribution to the simulation example in Section 6.

Funding

The authors would like to thank DEMCON Advanced Mechatronics, Best, The Netherlands, for the availability of the mechanical ventilation setup for the experimental study. Moreover, the authors would like to thank Prof. Dr. Ir. Johan Schoukens and Dr. Ir. Maarten Schoukens from the Eindhoven University of Technology, Eindhoven, The Netherlands, for the valuable and fruitful discussions. Furthermore, the authors would like to thank Niels Vervaet from the Eindhoven University of Technology, Eindhoven, The Netherlands, for his contribution to the simulation example in Section 6.

Keywords

Global stability
Hammerstein
Nonlinear feedback
Nonlinear systems
Numerical algorithms
Parameter identification
Steady-state errors
System identification
Wiener

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10.1016/j.automatica.2021.110012

published versionFinal published version, 1.21 MBLicence: CC BY

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title = "Computationally efficient identification of continuous-time Lur'e-type systems with stability guarantees",

abstract = "In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment.",

keywords = "Global stability, Hammerstein, Nonlinear feedback, Nonlinear systems, Numerical algorithms, Parameter identification, Steady-state errors, System identification, Wiener",

author = "Shakib, \{Mohammad Fahim\} and Pogromsky, \{Alexander Yu\} and Alexey Pavlov and \{van de Wouw\}, Nathan",

note = "Funding Information: The authors would like to thank DEMCON Advanced Mechatronics, Best, The Netherlands, for the availability of the mechanical ventilation setup for the experimental study. Moreover, the authors would like to thank Prof. Dr. Ir. Johan Schoukens and Dr. Ir. Maarten Schoukens from the Eindhoven University of Technology, Eindhoven, The Netherlands, for the valuable and fruitful discussions. Furthermore, the authors would like to thank Niels Vervaet from the Eindhoven University of Technology, Eindhoven, The Netherlands, for his contribution to the simulation example in Section 6. ",

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AU - Pogromsky, Alexander Yu

AU - Pavlov, Alexey

AU - van de Wouw, Nathan

N1 - Funding Information: The authors would like to thank DEMCON Advanced Mechatronics, Best, The Netherlands, for the availability of the mechanical ventilation setup for the experimental study. Moreover, the authors would like to thank Prof. Dr. Ir. Johan Schoukens and Dr. Ir. Maarten Schoukens from the Eindhoven University of Technology, Eindhoven, The Netherlands, for the valuable and fruitful discussions. Furthermore, the authors would like to thank Niels Vervaet from the Eindhoven University of Technology, Eindhoven, The Netherlands, for his contribution to the simulation example in Section 6.

PY - 2022/2

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N2 - In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment.

AB - In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment.

KW - Global stability

KW - Hammerstein

KW - Nonlinear feedback

KW - Nonlinear systems

KW - Numerical algorithms

KW - Parameter identification

KW - Steady-state errors

KW - System identification

KW - Wiener

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M3 - Article

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SN - 0005-1098

VL - 136

JO - Automatica

JF - Automatica

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ER -

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

Abstract

Bibliographical note

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Keywords

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