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This study presents incremental correction methods for refining neuralnetwork parameters or control functions entering into a continuous-time dynamicsystem to achieve improved solution accuracy in satisfying the interim pointconstraints placed on the performance output variables. The proposed approachis to linearise the dynamics around the baseline values of its arguments, andthen to solve for the corrective input required to transfer the perturbedtrajectory 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 compensatefor the prediction errors of pre-trained neural networks in real-timeapplications where high accuracy of the prediction of dynamical systems atprescribed time points is imperative. In this regard, the online updateapproach can be useful for enhancing overall targeting accuracy offinite-horizon control subject to point constraints using a neural policy.Numerical example demonstrates the effectiveness of the proposed approach in anapplication to a powered descent problem at Mars.

Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction