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- W4297725439 abstract "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." @default.
- W4297725439 created "2022-09-30" @default.
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- W4297725439 date "2022-09-08" @default.
- W4297725439 modified "2023-10-18" @default.
- W4297725439 title "Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction" @default.
- W4297725439 doi "https://doi.org/10.48550/arxiv.2209.03698" @default.
- W4297725439 hasPublicationYear "2022" @default.
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