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
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A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.
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.
Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.
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Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.