Failure Accommodation in Digital Flight Control Systems by Bayesian Decision Theory

A design method for digital control systems which is optimally tolerant of failures in aircraft sensors is presented. The functions of this system are accomplished with software instead of the popular and costly technique of hardware duplication. The approach taken, based on M-ary hypothesis testing, results in a bank of Kalman filters operating in parallel. A moving window of the innovations of each Kalman filter drives a detector that decides the failure state of the system. The detector calculates the likelihood ratio for each hypothesis corresponding to a specific failure state of the system. It also selects the most likely state estimate in the Bayesian sense from the bank of Kalman filters. The system can compensate for hardover as well as increased noise-type failures by computing the likelihood ratios as generalized likelihood ratios. The design method is applied to the design of a fault tolerant control system for a current configuration of the space shuttle orbiter at Mach 5 and 120,000 ft. The failure detection capabilities of the system are demonstrated using a real-time simulation of the system with noisy sensors.