Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations
In this paper, we introduce controlled semiautonomous neural ordinary differential equations (controlled SA-NODEs) for the approximation and learning of nonlinear controlled dynamical systems. The proposed framework extends semiautonomous neural ODEs to control-affine systems while preserving reduced parameter complexity through time-independent trainable coefficients. We establish a universal approximation theorem showing that controlled SA-NODEs approximate trajectories of nonlinear controlled systems uniformly on compact sets of initial conditions and admissible controls. Under additional Sobolev and Barron regularity assumptions, we derive quantitative approximation estimates of order $\mathcal{O}(P^{-1/2}+Q^{-1/2})$. We further prove that approximate controllability properties of the original nonlinear system are preserved under the controlled SA-NODE approximation. Numerical experiments on controlled pendulum and Duffing oscillator systems demonstrate that the proposed framework achieves accurate trajectory reconstruction and controllability performance with significantly fewer trainable parameters than classical neural ODE architectures.