Multiple Model Adaptive Estimation Applied to the LAMBDA URV for Failure Detection and Identification
Abstract
Multiple Model Adaptive Estimation (MMAE) is a method of estimating unknown system parameters by modeling all possible parameter configurations in several models. 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 operated by Wright Laboratory Flight Controls Division at Wright-Patterson Air Force Base, Ohio. 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. In this research, the MMAE algorithm is extended for the identification of dual failures through the use of a hierarchical structure of filter banks. The ability of the MMAE to identify dual failures in the face of wind gust uncertainty and sensor noise is investigated. Aircraft state excitation is required for effective MMAE performance. Therefore, the form of an optimal input dither signal is derived through extensive experimentation. Dither signals are applied to the command inputs of a Quantitative Feedback Theory flight control system which controls pitch rate, roll rate, and sideslip angle. In particular, the MMAE performance is studied as sinusoidal dither inputs are varied in frequency and magnitude. An analysis of tuning techniques for the elemental filters within the MMAE is presented.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 02, 1993
- Accession Number
- ADA274078
Entities
People
- David W. Lane
Organizations
- Air Force Institute of Technology