Data-driven quasi-LPV model predictive control using koopman operator techniques

A fast data-driven extension of the velocity-based quasi-linear parameter-varying model predictive control (qLMPC) approach is proposed for scenarios where first principles models are not available or are computationally too expensive. We use tools from the recently proposed Koopman operator framework to identify a quasi-linear parameter-varying model (in input/output and state-space form) by choosing the observables from physical insight. An online update strategy to adapt to changes in the plant dynamics is also proposed. The approach is validated experimentally on a strongly nonlinear 3-degree-of-freedom Control Moment Gyroscope, showing remarkable tracking performance.

Subjects

Nonlinear predictive control

Linear parameter-varying systems

Data-driven control

Koopman operator

Adaptive control

DDC Class

600: Technik

620: Ingenieurwissenschaften

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publishedVersion

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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1-s2.0-S2405896320322795-main.pdf

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754.64 KB

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Data-driven quasi-LPV model predictive control using koopman operator techniques