Date of Graduation
The research in this document focuses on the performance of a neural network-based fault tolerant system within a flight control system. This fault tolerant flight control system integrates sensor and actuator failure detection, identification, and accommodation (SFDIA and AFDIA). The SFDIA task is achieved by incorporating a main neural network (MNN) and a set of n decentralized neural networks (DNNs) for a system with n sensors assumed to be without physical redundancy. Particularly, the purpose of the MNN is to detect a wide variety of sensor failures while the purpose of the DNNs is to identify the particular sensor that has failed and accommodate for the failure. The AFDIA scheme also implements a MNN with three neural network controllers (NNCs). The function of NNCs is to regain equilibrium and to compensate for the pitching, rolling, and yawing moments induced by the failure. The NNs are trained on-line using the Extended Back-Propagation Algorithm (EBPA). Because of the on-line learning, neural estimators and controllers have the capability of adapting to changes in the aircraft dynamics and/or modeling discrepancies between the actual aircraft and its mathematical model. This factor makes neural estimators and controllers an attractive option for fault tolerant flight control system. Particular emphasis is placed in this study toward improving the performance of the SFDIA scheme in the presence of ramp-type soft failures which are hard to detect as well as achieving an efficient integration between SFDIA and AFDIA without degradation of performance in terms of false alarm rates and incorrect failure identification.
An, Younghwan, "A design of fault tolerant flight control systems for sensor and actuator failures using on-line learning neural networks." (1998). Graduate Theses, Dissertations, and Problem Reports. 8404.