SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
3 Pith papers cite this work. Polarity classification is still indexing.
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SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
citing papers explorer
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Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control
SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.
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Adaptive Control with Sparse Identification of Nonlinear Dynamics
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.