Peter K. Eide

Date of Award


Document Type


Degree Name

Master of Science in Electrical Engineering


Department of Electrical and Computer Engineering

First Advisor

Peter Maybeck, PhD


A Multiple Model Adaptive Estimation (MMAE) algorithm is implemented with the fully nonlinear six-degree-of-motion, Simulation Rapid-Prototyping Facility (SRF) VISTA F-16 software simulation tool. The algorithm is demonstrated to be capable of identifying flight critical aircraft actuator and sensor failures at a low dynamic pressure (20,000 ft, .4 Mach). Research included single and dual complete failures. Tuning methods for accommodating model mismatch, including addition of discrete dynamics pseudonoise and continuous measurement pseudonoise, are discussed and demonstrated. Scalar residuals within each filter are also examined and characterized for possible use as an additional failure declaration voter. Robustness to sensor failures provided by MMAE-based control is also demonstrated. An investigation of algorithm performance off the nominal design conditions is accomplished as a first step towards full flight envelope coverage. The algorithm is composed of a bank of Kalman filters modeled to match particular hypotheses of the real world. Each presumes a single failure in one of the flight critical actuators (left-right stabilators, left-right flaperon, rudder), or sensors (forward velocity, angle of attack, pitch rate, normal acceleration, roll rate, yaw rate, and lateral acceleration), and one presumes no failure. For dual failures, a hierarchical structure is used to keep the number of on-line filters to a minimum. This research advances the technology by testing algorithm performance against the most complete simulation model currently available.

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