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  4. Optimal operation and control of towing kites using online and offline Gaussian process learning supported model predictive control
 
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Optimal operation and control of towing kites using online and offline Gaussian process learning supported model predictive control

Publikationstyp
Conference Paper
Date Issued
2022-06
Sprache
English
Author(s)
Eckel, Christina  
Maiworm, Michael  
Findeisen, Rolf  
Institut
Elektrische Energietechnik E-6  
TORE-URI
http://hdl.handle.net/11420/13630
Start Page
2637
End Page
2643
Citation
American Control Conference (ACC 2022)
Contribution to Conference
American Control Conference, ACC 2022  
Publisher DOI
10.23919/ACC53348.2022.9867371
Scopus ID
2-s2.0-85138489869
Publisher
IEEE
Controlling wind kites requires accurate models, both for safe operation, as well as for thrust maximization. To this end, we present a trajectory tracking model predictive control (MPC) approach in combination with Gaussian processes for model learning. Since perfect prediction models are usually unavailable, we use a hybrid model approach consisting of a physical base model extended by Gaussian processes that learn the model-plant mismatch. This allows for the computation of optimized improved reference trajectories, compared to the nominal model case. We furthermore outline an online-learning trajectory tracking MPC approach, which updates the process model recursively taking new measurements into account if the prediction error becomes too large. In simulations we show that even for large model-plant mismatches correct and safe operation can be achieved using the hybrid model in the MPC.
Subjects
Trajectory tracking
Computational modeling
Measurement uncertainty
Training data
Gaussian processes
Predictive models
Stability analysis
DDC Class
004: Informatik
510: Mathematik
600: Technik
620: Ingenieurwissenschaften
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