|Publisher DOI:||10.23919/ACC53348.2022.9867371||Title:||Optimal operation and control of towing kites using online and offline Gaussian process learning supported model predictive control||Language:||English||Authors:||Eckel, Christina
|Keywords:||Trajectory tracking; Computational modeling; Measurement uncertainty; Training data; Gaussian processes; Predictive models; Stability analysis||Issue Date:||Jun-2022||Publisher:||IEEE||Source:||American Control Conference (ACC 2022)||Abstract (english):||
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.
|Conference:||American Control Conference, ACC 2022||URI:||http://hdl.handle.net/11420/13630||ISBN:||978-1-6654-5196-3||Institute:||Elektrische Energietechnik E-6||Document Type:||Article|
|Appears in Collections:||Publications without fulltext|
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