Current validation and verification (V&V) activities in aerospace industry mostly rely on time-consuming simulation-based tools. These classical Monte Carlo approaches have been widely used for decades to assess performance of AOCS/GNC systems containing multiple uncertain parameters. They are able to quantify the probability of sufficiently frequent phenomena, but they may fail in detecting rare but critical combinations of parameters. As the complexity of modern space systems increases, this limitation plays an ever-increasing role. In recent years, model-based worst-case analysis methods have reached a good level of maturity. Without the need of simulations, these tools can fully explore the space of all possible combinations of uncertain parameters and provide guaranteed mathematical bounds on robust stability margins and worst-case performance levels.Problematic parameter configurations, identified using these methods, can be used to guide the final Monte Carlo campaigns, thereby drastically shortening the standard V&V process. A limitation of classical model-based worst-case analysis methods is that they assume the uncertain parameters can take any value within a given range with equal probability. The probability of occurrence of a worst-case parameter combination is thus not measured. A system design can therefore be rejected based on a very rare and extremely unlikely scenario. Probabilistic μ-analysis combines worst-case information with probabilistic information. It tempts to bridges the analysis gap between efficient Monte Carlo simulations and deterministic μ-analysis. This research makes advances in probabilistic µ-analysis to develop new cheap and reliable tools to improve the characterization of rare but nonetheless possible events. This to tighten the aforementioned V&V analysis gap. The seminar will focus on the recently developed probabilistic gain, phase, disk and delay margin analysis algorithms.
Dates
Intervenants
Franca SOMERS