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洞察 - Stochastic Data-Driven Predictive Control
Stochastic Data-Driven Predictive Control with Equivalent Performance to Stochastic Model Predictive Control
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.
Data-Driven Stochastic Predictive Control with Chance Constraint Satisfaction Using Identified Multi-Step Predictors
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.
