A novel data-driven autoregressive estimator reconstructs unknown inputs to LTI MIMO systems and is strictly stable if and only if the system's invariant zeros lie inside the unit circle, a condition verifiable purely from input-output data.
Data-driven simulation and control,
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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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Data-Driven Unknown Input Reconstruction for MIMO Systems with Convergence Guarantees
A novel data-driven autoregressive estimator reconstructs unknown inputs to LTI MIMO systems and is strictly stable if and only if the system's invariant zeros lie inside the unit circle, a condition verifiable purely from input-output data.
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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.