Computational Method for Desensitized Optimal Guidance Using Direct Collocation

TL;DR

This paper introduces a computational approach for desensitized optimal guidance that enhances robustness to uncertainties using adaptive Gaussian quadrature collocation and shrinking horizon control, demonstrated on navigation and reentry problems.

Contribution

It develops a novel desensitized guidance method combining adaptive collocation and shrinking horizon control to improve robustness without high computational costs.

Findings

Method reduces sensitivity to model uncertainties.

Tighter trajectory envelopes achieved in simulations.

Performance demonstrated on navigation and reentry examples.

Abstract

A computational method is developed for desensitized optimal guidance using adaptive Gaussian quadrature collocation. The method computes a reference trajectory that reduces the sensitivity to uncertainties in the dynamic model by augmenting the objective functional to explicitly penalize the sensitivity of the state with respect to uncertain parameters. Using this desensitized reference trajectory as a starting point, the desensitized optimal guidance method developed in this paper computes a new optimal control on the remaining horizon at specified guidance update times. This shrinking horizon optimal control problem is solved using a Legendre-Gauss-Radau collocation method where, at each guidance update, a reduced-horizon mesh is determined by remapping the mesh to the remaining horizon and deleting the portion of the mesh associated with the expired portion of the horizon. The resulting guidance solution is found to improve robustness to external disturbances and modeling errors. The method is demonstrated on two numerical examples. The first example is Zermelo's navigation problem which illustrates the behavior of the method on a simple example. The second example is an atmospheric reentry problem that demonstrates the performance of the method on a more complex problem. For both examples, the dynamics are simulated in the presence of parameter uncertainties in the dynamic model, and Monte Carlo analysis is performed. The results show that the method developed in this paper produces tighter trajectory envelopes and smaller terminal state errors without significantly increasing the computational burden when compared with a method that does not penalize sensitivities.