Model Predictive Control: Classical, Robust and Stochastic

By B. Kouvaritakis, M. Cannon. 2015

358 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3a5df7363b34cdb860af841734216177ad6e60df

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1. Johannes Köhler
2. Frank Allgöwer
3. Matthias Albrecht Müller
4. Julian Berberich
5. Florian Dörfler

Citations in the corpus

• Data-Driven Model Predictive Control With Stability and Robustness Guarantees (2019, methodology): “In model-based robust MPC schemes, similar bounds are typically used to propagate uncertainty, where the role of the weighting vector α to account for multiplicative uncertainty is replaced by the state x and a model-based uncertainty description (compare [34] for details).”
• Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework (2016, unknown): “In particular, the robust MPC can be designed on a nominal model where the tightening of the state constraints is computed to guarantee that the original system satisfies the nominal constraints for all the disturbance values [38].”