  This study presents incremental correction methods for refining neural
network parameters or control functions entering into a continuous-time dynamic
system to achieve improved solution accuracy in satisfying the interim point
constraints placed on the performance output variables. The proposed approach
is to linearise the dynamics around the baseline values of its arguments, and
then to solve for the corrective input required to transfer the perturbed
trajectory to precisely known or desired values at specific time points, i.e.,
the interim points. Depending on the type of decision variables to adjust,
parameter correction and control function correction methods are developed.
These incremental correction methods can be utilised as a means to compensate
for the prediction errors of pre-trained neural networks in real-time
applications where high accuracy of the prediction of dynamical systems at
prescribed time points is imperative. In this regard, the online update
approach can be useful for enhancing overall targeting accuracy of
finite-horizon control subject to point constraints using a neural policy.
Numerical example demonstrates the effectiveness of the proposed approach in an
application to a powered descent problem at Mars.
