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.
Lyapunov-stable neural control for state and output feedback: A novel formulation for efficient synthesis and verification
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LightCROWN computes tighter Jacobian bounds for neural networks with smooth nonlinear activations by exploiting their analytical properties, raising verification success rates for neural control barrier functions up to 100% on benchmark control systems.
Learned Lyapunov functions, residual SAC policies, and PINNs are combined with a Slotine-Li controller and a closed-form safety filter to improve tracking on uncertain Euler-Lagrange systems while retaining stability guarantees.
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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Efficient Verification of Neural Control Barrier Functions with Smooth Nonlinear Activations
LightCROWN computes tighter Jacobian bounds for neural networks with smooth nonlinear activations by exploiting their analytical properties, raising verification success rates for neural control barrier functions up to 100% on benchmark control systems.
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Learned Lyapunov Shielding for Adaptive Control
Learned Lyapunov functions, residual SAC policies, and PINNs are combined with a Slotine-Li controller and a closed-form safety filter to improve tracking on uncertain Euler-Lagrange systems while retaining stability guarantees.