A neural-network framework learns parameter-dependent controllers and Lyapunov functions for nonlinear parameter-varying systems, improving on sum-of-squares methods in applicability and scalability.
Safe control with learned certificates: A survey of neural Lyapunov, barrier, and contraction methods for robotics and control,
3 Pith papers cite this work. Polarity classification is still indexing.
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FAIL iteratively learns maximal state-control invariant sets from one-step failing state-input pairs for deterministic LTI systems with polytopic constraints, proving monotonic convergence to the true MSCI without dynamics knowledge.
Set-based safety verification with latent zonotopes achieves 5/5 collision-free passages in a 16D quadrotor task versus 1/5 for point evaluation by detecting blind spots and adapting per-head margins up to 12x.
citing papers explorer
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Neural-NPV Control: Learning Parameter-Dependent Controllers and Lyapunov Functions with Neural Networks
A neural-network framework learns parameter-dependent controllers and Lyapunov functions for nonlinear parameter-varying systems, improving on sum-of-squares methods in applicability and scalability.
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Failure-Aware Iterative Learning of State-Control Invariant Sets
FAIL iteratively learns maximal state-control invariant sets from one-step failing state-input pairs for deterministic LTI systems with polytopic constraints, proving monotonic convergence to the true MSCI without dynamics knowledge.
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From Points to Sets: Set-Based Safety Verification in the Latent Space
Set-based safety verification with latent zonotopes achieves 5/5 collision-free passages in a 16D quadrotor task versus 1/5 for point evaluation by detecting blind spots and adapting per-head margins up to 12x.