Desensitized Optimal Guidance Using Adaptive Radau Collocation

TL;DR

This paper introduces a guidance method that minimizes sensitivity to model parameter variations by integrating adaptive Radau collocation with desensitized optimal control, improving terminal state consistency in dynamic systems.

Contribution

It presents a novel combination of adaptive Radau collocation and desensitized optimal control for guidance, enhancing robustness against parameter uncertainties.

Findings

Reduces variations in terminal state compared to previous methods

Demonstrates effectiveness through Monte Carlo simulation

Improves robustness of guidance in uncertain dynamic models

Abstract

An optimal guidance method is developed that reduces sensitivity to parameters in the dynamic model. The method combines a previously developed method for guidance and control using adaptive Legendre-Gauss-Radau (LGR) collocation and a previously developed approach for desensitized optimal control. Guidance updates are performed such that the desensitized optimal control problem is re-solved on the remaining horizon at the start of each guidance cycle. The effectiveness of the method is demonstrated on a simple example using Monte Carlo simulation. It is found that the method reduces variations in the terminal state as compared to either desensitized optimal control without guidance updates or a previously developed method for optimal guidance and control.