Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

15 Nov 2017Eurika KaiserJ. Nathan KutzSteven L. Brunton

The data-driven discovery of dynamics via machine learning is currently pushing the frontiers of modeling and control efforts, and it provides a tremendous opportunity to extend the reach of model predictive control. However, many leading methods in machine learning, such as neural networks, require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and often do not generalize beyond the attractor where models are trained... (read more)

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