Informativity conditions enable unique output prediction for LTI systems from data without uniquely identifying the underlying dynamics.
From model-based control to data-driven control: Survey, classification and perspective,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
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ICODE-MPPI uses Input Concomitant Neural ODEs to learn residual dynamics and reduce vehicle cross-tracking error by up to 69% under disturbances compared with standard MPPI.
A new regularized covariance parameterization enables effective direct data-driven LQR control for ill-conditioned data, shown equivalent to indirect Tikhonov-regularized LQR and extended to nonlinear systems via Koopman embedding.
citing papers explorer
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Informativity for Data-driven Prediction
Informativity conditions enable unique output prediction for LTI systems from data without uniquely identifying the underlying dynamics.
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Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
ICODE-MPPI uses Input Concomitant Neural ODEs to learn residual dynamics and reduce vehicle cross-tracking error by up to 69% under disturbances compared with standard MPPI.
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On Tikhonov Regularization for Direct and Indirect Data-Driven LQR Control
A new regularized covariance parameterization enables effective direct data-driven LQR control for ill-conditioned data, shown equivalent to indirect Tikhonov-regularized LQR and extended to nonlinear systems via Koopman embedding.