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.
翻译:本研究报告介绍了改进神经网络参数或控制功能的渐进式纠正方法,这些方法将进入一个连续时间动态系统,以便在满足对性能输出变量的临时点限制时,实现更好的解决方案准确性。拟议的方法是将其参数基线值周围的动态线性化,然后解决将扰动轨迹转换到具体时间点(即临时点)精确已知或理想值所需的纠正性投入。根据调整的决定变量类型,制定了参数校正和控制功能校正方法。这些递增式纠正方法可以作为一种手段,用以补偿实时应用中预先训练的神经网络的预测错误,因为必须高精确度预测在规定时间点对动态系统进行预测。在这方面,在线更新方法可以有助于加强定点控制定点的精确性,但需使用神经政策来点限制。数字实例表明拟议方法在火星电源下的问题应用中的有效性。