Multiple model adaptive estimation with filter spawning

TL;DR: In this paper, multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16, assuming a failure to free stream.

Abstract: Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream." Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuaton-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared with what is possible without spawning additional filters, making partial failure adaptation a viable methodology.