  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.
