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Harnessing Data for Accelerating Model Predictive Control by Constraint Removal
Model predictive control (MPC) solves a receding-horizon optimization problem in real-time, which can be computationally demanding when there are thousands of constraints.
Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator
Risk-aware control, though with promise to tackle unexpected events, requires a known exact dynamical model.
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