A min-max robust DDPC method is introduced via uncertainty sets derived from non-unique behavioral solutions, yielding convex reformulations, feedback extensions, and performance guarantees under bounded noise.
Data-driven predictive control in a stochastic setting: a unified framework,
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A local linearization of the implicit ARX predictor yields a Jacobian that approximates control-relevant uncertainty and shapes kernel regularization in data-driven MPC.
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On Min-Max Robust Data-Driven Predictive Control Considering Non-Unique Solutions to Behavioral Representation
A min-max robust DDPC method is introduced via uncertainty sets derived from non-unique behavioral solutions, yielding convex reformulations, feedback extensions, and performance guarantees under bounded noise.
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Local Sensitivity Analysis for Kernel-Regularized ARX Predictors in Data-Driven Predictive Control
A local linearization of the implicit ARX predictor yields a Jacobian that approximates control-relevant uncertainty and shapes kernel regularization in data-driven MPC.