Data-Driven quasi-LPV Model Predictive Control Using Koopman Operator Techniques

Cisneros, Pablo S. G.; Datar, Adwait; Göttsch, Patrick; Werner, Herbert

doi:10.1016/j.ifacol.2020.12.1676

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…This suggests its applicability to distributed real-world systems with significant parameter uncertainty and limited data availability. We expect that the proposed framework can be extended to handle timevarying flow rates and consumer loads locally using an LPV approach as described in Cisneros et al (2020), or globally using the Bilinear DMD algorithm described in Goldschmidt et al (2021).…”

Section: Discussionmentioning

confidence: 99%

HAVOK Model Predictive Control for Time-Delay Systems with Applications to District Heating

Jensen¹,

Frederiksen²,

Kallesøe³

et al. 2022

Preprint

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A computationally efficient Model-Predictive Control (MPC) approach is proposed for systems with unknown delay using only input/output data. We use the Koopman operator framework and the related Hankel Alternative View of Koopman (HAVOK) algorithm to identify a model in a basis of projected time-delay coordinates and demonstrate a novel MPC structure that reduces and bounds the computational complexity. The proposed HAVOK-MPC approach is validated experimentally on a laboratory-scale District Heating System (DHS), demonstrating excellent prediction and tracking performance while only requiring knowledge of a conservative upper bound on the system delay.

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Section: Discussionmentioning

confidence: 99%

HAVOK Model Predictive Control for Time-Delay Systems with Applications to District Heating

Jensen¹,

Frederiksen²,

Kallesøe³

et al. 2022

Preprint

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“…control applications (e.g. mechatronic systems (Abraham and Murphey, 2019), (Cisneros et al, 2020), distributed parameter systems (Klus et al, 2020)). For practical use, a finite number of observables needs to be selected.…”

Section: Introductionmentioning

confidence: 99%

Finite Dimensional Koopman Form of Polynomial Nonlinear Systems

Iacob¹,

Schoukens²,

Tóth³

2023

Preprint

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The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-called observable functions. While there is an extensive theory on infinite dimensional representations in the operator sense, there are few constructive results on how to select the observables to realize them. When it comes to the possibility of finite Koopman representations, which are highly important form a practical point of view, there is no constructive theory. Hence, in practice, often a data-based method and ad-hoc choice of the observable functions is used. When truncating to a finite number of basis, there is also no clear indication of the introduced approximation error. In this paper, we propose a systematic method to compute the finite dimensional Koopman embedding of a specific class of polynomial nonlinear systems in continuous-time such that, the embedding, without approximation, can fully represent the dynamics of the nonlinear system.

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Data-Driven model identification and efficient MPC via quasi-linear parameter varying representation for ORC waste heat recovery system

Shi

Zhang²,

Chen³

et al. 2023

Energy

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Data-Driven quasi-LPV Model Predictive Control Using Koopman Operator Techniques

Cited by 12 publications

References 23 publications

HAVOK Model Predictive Control for Time-Delay Systems with Applications to District Heating

HAVOK Model Predictive Control for Time-Delay Systems with Applications to District Heating

Finite Dimensional Koopman Form of Polynomial Nonlinear Systems

Data-Driven model identification and efficient MPC via quasi-linear parameter varying representation for ORC waste heat recovery system

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