Multiobjective worst-case analysis of a re-entry vehicle control law

This paper reports results of a joint study between ESA and the University of Leicester on worst-case analysis of NDI control laws for an industrial standard Reusable Launch Vehicle. Multiple performance objectives over a particular phase of the atmospheric re-entry are considered simultaneously in the analysis, yielding valuable information about the trade-offs involved in satisfying different clearance criteria. Two different multiobjective optimisation algorithms are employed to identify the pareto front of the multiple performance objectives. In the initial analysis, a fast, elitist, evolutionary multiobjective optimisation algorithm known as nondominated sorting genetic algorithm (NSGA-II) is employed. A hybrid multi objective optimisation algorithm which adaptively switches between three different strategies such as NSGA-II, differential evolution and the metropolis algorithm, is also developed and applied to the clearance problem. The results of our analysis show that the proposed optimisation-based approach has the potential to significantly improve both the reliability and efficiency of the flight clearance process for future re-entry vehicles.