Cisneros, Pablo S. G.Pablo S. G.CisnerosDatar, AdwaitAdwaitDatarGöttsch, PatrickPatrickGöttschWerner, HerbertHerbertWerner2021-01-132021-01-132020IFAC World Congress 53 (2): 6062-6068 (2020)http://hdl.handle.net/11420/8429A 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. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND licenseen2405-89632020260626068Elsevierhttps://creativecommons.org/licenses/by-nc-nd/4.0/Nonlinear predictive controlLinear parameter-varying systemsData-driven controlKoopman operatorAdaptive controlTechnikIngenieurwissenschaftenConference Paper10.15480/882.354010.1016/j.ifacol.2020.12.167610.15480/882.3540Conference Paper