Failure Identification using Multiple Model Adaptive Estimation for the LAMBDA Flight Vehicle
Abstract
This study develops and investigates the performance of a Multiple Model Adaptive Estimator (MMAE) to detect and identify control surface and sensor failures on the LAMBDA flight vehicle (a URV developed by Wright Laboratories). The MMAE uses a bank of Kalman filters that predict the aircraft response to a given input, with each filter model based on a different failure hypothesis, and then forms the residual difference between the prediction and sensor measurements for each filter. The MMAE uses these residuals to determine the probabilities of the failures that are modeled by the Kalman filters. Initially the MMAE identified all these failures within 4 seconds of onset. Various performance improvement techniques were researched and the identification time was reduced to less than 2 seconds after failure onset. This improvement was mostly due to an increase in the penalty for measurement differences, and through returning of the Kalman filters. The MMAE performance was tested at the boundaries of the LAMBDA flight envelope, with good performance found at points close to the design flight condition. The performance at points that were far from the design flight condition indicates that gain scheduling is required to provide adequate performance across the entire envelope.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 04, 1992
- Accession Number
- ADA259137
Entities
People
- Peter D. Hanlon
Organizations
- Air Force Institute of Technology