Mismatch-Aware Adaptive Constraint Tightening (MACT) sets state-dependent safety margins using a derived T-squared scaling coefficient from model mismatch analysis, achieving full safety with 84% less wasted margin than fixed baselines in simulations.
Robust model predic- tive control of constrained linear systems with bounded disturbances
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A new tractable tube-based robust data-driven MPC for unknown discrete-time LTI systems using one noisy trajectory, simplex-constrained Hankel coefficients, and certified RPI sets to ensure recursive feasibility and practical ISS via a convex QP.
A data-driven procedure constructs robust positively invariant tube sets from noisy data of unknown LTI systems and certifies them for use in tube-based robust predictive control.
Derives a computable closed-form upper bound on Hausdorff distance for truncated mRPI sets that depends only on disturbance-set size and norm-dependent contraction rate, yielding an explicit horizon-selection rule.
citing papers explorer
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Mismatch-Aware Adaptive Constraint Tightening for Bicycle-Model Trajectory Optimization
Mismatch-Aware Adaptive Constraint Tightening (MACT) sets state-dependent safety margins using a derived T-squared scaling coefficient from model mismatch analysis, achieving full safety with 84% less wasted margin than fixed baselines in simulations.
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Tube-Based Robust Data-Driven Predictive Control
A new tractable tube-based robust data-driven MPC for unknown discrete-time LTI systems using one noisy trajectory, simplex-constrained Hankel coefficients, and certified RPI sets to ensure recursive feasibility and practical ISS via a convex QP.
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Data-Driven Synthesis of Robust Positively Invariant Sets from Noisy Data
A data-driven procedure constructs robust positively invariant tube sets from noisy data of unknown LTI systems and certifies them for use in tube-based robust predictive control.
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Explicit Bounds on the Hausdorff Distance for Truncated mRPI Sets via Norm-Dependent Contraction Rates
Derives a computable closed-form upper bound on Hausdorff distance for truncated mRPI sets that depends only on disturbance-set size and norm-dependent contraction rate, yielding an explicit horizon-selection rule.