Designing controllers that achieve task objectives while ensuring safety is a
key challenge in control systems. This work introduces Opt-ODENet, a Neural ODE
framework with a differentiable Quadratic Programming (QP) optimization layer
to enforce constraints as hard requirements. Eliminating the reliance on
nominal controllers or large datasets, our framework solves the optimal control
problem directly using Neural ODEs. Stability and convergence are ensured
through Control Lyapunov Functions (CLFs) in the loss function, while Control
Barrier Functions (CBFs) embedded in the QP layer enforce real-time safety. By
integrating the differentiable QP layer with Neural ODEs, we demonstrate
compatibility with the adjoint method for gradient computation, enabling the
learning of the CBF class-$\mathcal{K}$ function and control network
parameters. Experiments validate its effectiveness in balancing safety and
performance.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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