A fault tolerant flight control system for sensor and actuator failures using neural networks

In recent years neural networks have been proposed for identification and control of linear and non-linear dynamic systems. This paper describes 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 first task is achieved by incorporating a main neural network (MNN) and a set of n decentralized neural networks (DNNs) to create a system with n sensors which has the ability to detect a wide variety of sensor failures. The second scheme implements the same main neural network integrated with three neural network controllers. The contribution of this paper focuses on enhancements of the SFDIA scheme to allow the handling of soft failures as well as addressing the issue of integrating the SFDIA and the AFDIA schemes without degradation of performance in terms of false alarm rates and incorrect failure identification. The results of the simulation with different actuator and sensor failures with a non-linear aircraft model are presented and discussed.