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

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