Multiple Model Adaptive Estimation Using Filter Spawning

Multiple Model Adaptive Estimation 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 Multiple Model Adaptive Estimation (MMAE) bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuator-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\u27 estimates based upon a no-failure hypothesis (or, an effectiveness of 100%), a complete actuator failure hypothesis (or, an effectiveness of 0%), and the spawned filters\u27 partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared to what is possible without spawning additional filters, making partial failure adaptation a viable methodology in a manner heretofore unachieved