Data-Driven quasi-LPV Model Predictive Control Using Koopman Operator Techniques
| Autor: | Patrick Göttsch, Pablo S. G. Cisneros, Herbert Werner, Adwait Datar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science 020208 electrical & electronic engineering Observable 02 engineering and technology Extension (predicate logic) Tracking (particle physics) Data-driven Control moment gyroscope Nonlinear system Model predictive control 020901 industrial engineering & automation Operator (computer programming) Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering |
| Zdroj: | IFAC-PapersOnLine. 53:6062-6068 |
| ISSN: | 2405-8963 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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