Multiple model adaptive estimation applied to the LAMBDA URV for failure detection and identification

Multiple model adaptive estimation (MMAE) is a method of estimating unknown system parameters by modeling different discrete parameter values in several filters that are run in parallel and compared. The parameters for this research are failure status conditions associated with flight control actuators and sensors on the LAMBDA unmanned research vehicle, an experimental aircraft. Six actuator failures and eight sensor failures are modeled, along with the fully functional aircraft, in fifteen elemental Kalman filters. These filters propagate and update their own aircraft state estimates in real time. A probability computation representing the likelihood of each elemental filter´s match to the true condition of the aircraft is used to generate relative probabilities for each filter´s hypothesis. The MMAE algorithm is extended for the identification of dual failures through the use of a hierarchical structure of filter banks. Aircraft state excitation is required for effective MMAE performance. Sinusoidal dither signals are applied to the command inputs of a flight control system which controls pitch rate, roll rate, and sideslip angle