
A data-driven receding-horizon control method is proposed that produces identical control inputs as Stochastic Model Predictive Control (SMPC) for unknown stochastic linear time-invariant systems, while accounting for process noise, measurement noise, and uncertain initial conditions.


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stochastic-data-driven-predictive-control-with-equivalent-performance-to-stochastic-model-predictive-control


Stochastic Data-Driven Predictive Control with Equivalent Performance to Stochastic Model Predictive Control


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A novel data-driven stochastic model predictive control framework is proposed for uncertain linear systems with noisy output measurements. The approach leverages multi-step predictors to efficiently propagate uncertainty and ensure chance constraint satisfaction with minimal conservatism.


data-driven-stochastic-predictive-control-with-chance-constraint-satisfaction-using-identified-multi-step-predictors


Data-Driven Stochastic Predictive Control with Chance Constraint Satisfaction Using Identified Multi-Step Predictors
