Re-Entry Flight Clearance Using Interval Analysis

Abstract

Reentry flight is one of the most challenging parts in spaceflight missions. Before the actual flight, the vehicle has to be certified for safety of flight within its intended flight domain. This certification process, called reentry flight clearance, is based on accurate mathematical models of the vehicle and its environment. Current practice is to use model evaluations including a representative set of uncertainty parameter combinations in a Monte Carlo approach. The objective of the present paper is to evaluate the application of a promising new mathematical technique called interval analysis for reentry flight clearance. Whereas a Monte Carlo approach clears the vehicle only for a finite number of sets of uncertainty parameters, the new approach clears the vehicle within prespecified uncertainty parameter intervals. The new approach is applied to the reentry trajectory of the Delft Aerospace Reentry Test Demonstrator, a hypothetical ballistic reentry vehicle equipped with an attitude-stabilizing flight control system. As the clearance requirement, the stability of the system is chosen, which is evaluated using two different mathematical criteria: worst-case linear eigenvalue analysis and nonlinear Lyapunov analysis. The new approach, based on interval analysis, is applied in an evaluation of both criteria. Subsequently, Monte Carlo analysis was performed to verify the validity of these results.