  Neural ordinary differential equations (Neural ODEs) define continuous time
dynamical systems with neural networks. The interest in their application for
modelling has sparked recently, spanning hybrid system identification problems
and time series analysis. In this work we propose the use of a neural control
policy capable of satisfying state and control constraints to solve nonlinear
optimal control problems. The control policy optimization is posed as a Neural
ODE problem to efficiently exploit the availability of a dynamical system
model. We showcase the efficacy of this type of deterministic neural policies
in two constrained systems: the controlled Van der Pol system and a bioreactor
control problem. This approach represents a practical approximation to the
intractable closed-loop solution of nonlinear control problems.
