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Found 4 papers, 1 papers with code
Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty
We present a stochastic output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances and probabilistic chance constraints.
Offline Uncertainty Sampling in Data-driven Stochastic MPC
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise.
Diffeomorphically Learning Stable Koopman Operators
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling.
Data-driven tube-based stochastic predictive control
A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI) systems with unknown dynamics purely from data.
