Physically consistent modeling & identification of nonlinear friction with dissipative Gaussian processes

Rui Dai, Giulio Evangelisti, Sandra Hirche
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR 242:1415-1426, 2024.

Abstract

Friction modeling has always been a challenging problem due to the complexity of real physical systems. Although a few state-of-the-art structured data-driven methods show their efficiency in nonlinear system modeling, deterministic passivity as one of the significant characteristics of friction is rarely considered in these methods. To address this issue, we propose a Gaussian Process based model that preserves the inherent structural properties such as passivity. A matrix-vector physical structure is considered in our approaches to ensure physical consistency, in particular, enabling a guarantee of positive semi-definiteness of the damping matrix. An aircraft benchmark simulation is employed to demonstrate the efficacy of our methodology. Estimation accuracy and data efficiency are increased substantially by considering and enforcing more structured physical knowledge. Also, the fulfillment of the dissipative nature of the aerodynamics is validated numerically.

Cite this Paper

BibTeX
@InProceedings{pmlr-v242-dai24a, title = {Physically consistent modeling \& identification of nonlinear friction with dissipative {G}aussian processes}, author = {Dai, Rui and Evangelisti, Giulio and Hirche, Sandra}, booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference}, pages = {1415--1426}, year = {2024}, editor = {Abate, Alessandro and Cannon, Mark and Margellos, Kostas and Papachristodoulou, Antonis}, volume = {242}, series = {Proceedings of Machine Learning Research}, month = {15--17 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v242/dai24a/dai24a.pdf}, url = {https://proceedings.mlr.press/v242/dai24a.html}, abstract = {Friction modeling has always been a challenging problem due to the complexity of real physical systems. Although a few state-of-the-art structured data-driven methods show their efficiency in nonlinear system modeling, deterministic passivity as one of the significant characteristics of friction is rarely considered in these methods. To address this issue, we propose a Gaussian Process based model that preserves the inherent structural properties such as passivity. A matrix-vector physical structure is considered in our approaches to ensure physical consistency, in particular, enabling a guarantee of positive semi-definiteness of the damping matrix. An aircraft benchmark simulation is employed to demonstrate the efficacy of our methodology. Estimation accuracy and data efficiency are increased substantially by considering and enforcing more structured physical knowledge. Also, the fulfillment of the dissipative nature of the aerodynamics is validated numerically.} }
Endnote
%0 Conference Paper %T Physically consistent modeling & identification of nonlinear friction with dissipative Gaussian processes %A Rui Dai %A Giulio Evangelisti %A Sandra Hirche %B Proceedings of the 6th Annual Learning for Dynamics & Control Conference %C Proceedings of Machine Learning Research %D 2024 %E Alessandro Abate %E Mark Cannon %E Kostas Margellos %E Antonis Papachristodoulou %F pmlr-v242-dai24a %I PMLR %P 1415--1426 %U https://proceedings.mlr.press/v242/dai24a.html %V 242 %X Friction modeling has always been a challenging problem due to the complexity of real physical systems. Although a few state-of-the-art structured data-driven methods show their efficiency in nonlinear system modeling, deterministic passivity as one of the significant characteristics of friction is rarely considered in these methods. To address this issue, we propose a Gaussian Process based model that preserves the inherent structural properties such as passivity. A matrix-vector physical structure is considered in our approaches to ensure physical consistency, in particular, enabling a guarantee of positive semi-definiteness of the damping matrix. An aircraft benchmark simulation is employed to demonstrate the efficacy of our methodology. Estimation accuracy and data efficiency are increased substantially by considering and enforcing more structured physical knowledge. Also, the fulfillment of the dissipative nature of the aerodynamics is validated numerically.
APA
Dai, R., Evangelisti, G. & Hirche, S.. (2024). Physically consistent modeling & identification of nonlinear friction with dissipative Gaussian processes. Proceedings of the 6th Annual Learning for Dynamics & Control Conference, in Proceedings of Machine Learning Research 242:1415-1426 Available from https://proceedings.mlr.press/v242/dai24a.html.

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