Time-Domain Model Matching Under General Norms via Sparse Matrix Methods

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Standard

Time-Domain Model Matching Under General Norms via Sparse Matrix Methods. / Handler, Johannes; Harker, Matthew; Rath, Gerhard.
2023 12th Mediterranean Conference on Embedded Computing (MECO). 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvard

Handler, J, Harker, M & Rath, G 2023, Time-Domain Model Matching Under General Norms via Sparse Matrix Methods. in 2023 12th Mediterranean Conference on Embedded Computing (MECO). 12th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 6/06/23. https://doi.org/10.1109/MECO58584.2023.10155005

APA

Handler, J., Harker, M., & Rath, G. (2023). Time-Domain Model Matching Under General Norms via Sparse Matrix Methods. In 2023 12th Mediterranean Conference on Embedded Computing (MECO) https://doi.org/10.1109/MECO58584.2023.10155005

Vancouver

Handler J, Harker M, Rath G. Time-Domain Model Matching Under General Norms via Sparse Matrix Methods. In 2023 12th Mediterranean Conference on Embedded Computing (MECO). 2023 doi: 10.1109/MECO58584.2023.10155005

Author

Handler, Johannes ; Harker, Matthew ; Rath, Gerhard. / Time-Domain Model Matching Under General Norms via Sparse Matrix Methods. 2023 12th Mediterranean Conference on Embedded Computing (MECO). 2023.

Bibtex - Download

@inproceedings{86ab8b2ee83d4aa9a2b90f2f8ab35f8a,
title = "Time-Domain Model Matching Under General Norms via Sparse Matrix Methods",
abstract = "This paper presents a new approach to the task of time-domain model matching for state-space systems. The traditional problem formulation of designing a controller to match a reference model is relaxed to matching only a desired reference response. The presented algorithm then computes the feedback gain that delivers the best fit solution to the reference response under general norms. Additionally, the proposed discretization approach enables the employment of sparse matrix methods which enables a numerically efficient implementation. The new method is successfully verified using a random system. Additionally, an application example involving a simplified gantry crane system is presented, showcasing the practicality of the approach. Overall, the new method provides an intuitive and numerically efficient solution to the problem of time-domain model matching for state-space systems.",
author = "Johannes Handler and Matthew Harker and Gerhard Rath",
year = "2023",
month = jun,
day = "6",
doi = "10.1109/MECO58584.2023.10155005",
language = "English",
booktitle = "2023 12th Mediterranean Conference on Embedded Computing (MECO)",
note = "12th Mediterranean Conference on Embedded Computing (MECO), MECO ; Conference date: 06-06-2023 Through 10-06-2023",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Time-Domain Model Matching Under General Norms via Sparse Matrix Methods

AU - Handler, Johannes

AU - Harker, Matthew

AU - Rath, Gerhard

N1 - Conference code: 12

PY - 2023/6/6

Y1 - 2023/6/6

N2 - This paper presents a new approach to the task of time-domain model matching for state-space systems. The traditional problem formulation of designing a controller to match a reference model is relaxed to matching only a desired reference response. The presented algorithm then computes the feedback gain that delivers the best fit solution to the reference response under general norms. Additionally, the proposed discretization approach enables the employment of sparse matrix methods which enables a numerically efficient implementation. The new method is successfully verified using a random system. Additionally, an application example involving a simplified gantry crane system is presented, showcasing the practicality of the approach. Overall, the new method provides an intuitive and numerically efficient solution to the problem of time-domain model matching for state-space systems.

AB - This paper presents a new approach to the task of time-domain model matching for state-space systems. The traditional problem formulation of designing a controller to match a reference model is relaxed to matching only a desired reference response. The presented algorithm then computes the feedback gain that delivers the best fit solution to the reference response under general norms. Additionally, the proposed discretization approach enables the employment of sparse matrix methods which enables a numerically efficient implementation. The new method is successfully verified using a random system. Additionally, an application example involving a simplified gantry crane system is presented, showcasing the practicality of the approach. Overall, the new method provides an intuitive and numerically efficient solution to the problem of time-domain model matching for state-space systems.

U2 - 10.1109/MECO58584.2023.10155005

DO - 10.1109/MECO58584.2023.10155005

M3 - Conference contribution

BT - 2023 12th Mediterranean Conference on Embedded Computing (MECO)

T2 - 12th Mediterranean Conference on Embedded Computing (MECO)

Y2 - 6 June 2023 through 10 June 2023

ER -

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