Stochastic Model Predictive Control: An Overview and Perspectives for Future Research
By Ali Mesbah. IEEE Control Systems, 2016
922 citations recorded by the source corpus.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/b9a97f803251a491e5964e4ac2f07b33bd9f14c9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Lukas Hewing
- Melanie Nicole Zeilinger
- Juraj Kabzan
- Jan-Hendrik Niemann
- Chao Ning
- Emanuele Garone
- Christof Schutte
- Ilya V. Kolmanovsky
Citations in the corpus
- Reference and command governors for systems with constraints: A survey on theory and applications (2017, positive): “One of the main advantages of RG and CG with respect to other more complex constrained control strategies, such as MPC, is the limited computational footprint of the online implementation.”
- Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “In recent years, mathematical programming techniques for decision making under uncertainty have gained tremendous popularity among the PSE community, as witnessed by various successful applications in process synthesis and design [9, 27], production scheduling and planning [7, 28], and process contr”
- Data-driven approximation of the Koopman generator: Model reduction, system identification, and control (2019, background): “In stochastic (or robust ) control (see [61, 62] for introductions), the goal is very often to steer the expected value to some desired value.”
- Cautious Model Predictive Control Using Gaussian Process Regression (2017, background): “An equivalent choice is given by linear disturbance feedback [16], which is popular, in particular, for linear systems, where it allows for convex optimization over the feedback gains [17].”
- Learning-Based Model Predictive Control: Toward Safe Learning in Control (2020, result): “requires a hard a priori bound covering all uncertainty, which can be conservative in practice. uncertainty and do not necessarily make use of hard bounds, which, however, renders a theoretical analysis more difficult and often results in less rigorous results (4).”