Probabilistic gain, phase and disk margins with application to AOCS validation

Monte-Carlo simulations play a key role in most Verification and Validation (V&V) processes. It is however time-consuming and may fail to detect rare worst-case configurations. In such cases, when applicable, µ-analysis offers a nice alternative but does not provide any quantification of the probability of occurrence of the identified worst-cases. A control system can then be invalidated on the basis of unlikely events. Probabilistic µ-analysis was introduced in this context 20 years ago to bridge the gap between the two techniques, but until recently no practical tools were available. This paper summarizes recent advances on this topic. A practical algorithm for probabilistic gain, phase and disk margins analysis is first proposed, and then applied to a satellite high pointing system involving uncertain flexible modes.