A stochastic MPC method precomputes a low-dimensional 'feature' feedback policy and an approximate chance-constrained set offline, enabling online optimization about 10x faster than full affine disturbance feedback SMPC.
Scenario-based probabilistic reachable sets for recursively feasible stochastic model predictive control
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Fast Stochastic MPC using Affine Disturbance Feedback Gains Learned Offline
A stochastic MPC method precomputes a low-dimensional 'feature' feedback policy and an approximate chance-constrained set offline, enabling online optimization about 10x faster than full affine disturbance feedback SMPC.