Development and Evaluation of Neural Network Models for Cost Reduction in Unmanned Air Vehicles

With a growing demand for cost reduction in unmanned air vehicles (UAVs), there has been considerable interest in exploiting existing aircraft technologies. This thesis focuses on two technologies: model-based sensor fault detection, isolation and accommodation (SFDIA) schemes and flush air data sensing (FADS) systems. In the aerospace industry, SFDIA is traditionally based on physical (sensor) redundancy. Unfortunately this approach can be inadequate in UAVs due to cost, weight and space implications. Consequently researchers have found the concept of ‘virtual’ sensor redundancy (i.e. model-based methods) an invaluable alternative to physical redundancy. Current model-based SFDIA schemes rely on linear time-invariant (LTI) models. In nonlinear, time-varying systems (such as aircraft), LTI-based methods can sometimes fail to give satisfactory results. New approaches make use of neural network (NN) models due to their nonlinear and adaptive structure. In this thesis, a NN-based SFDIA scheme is designed to detect single and multiple sensor faults in a nonlinear UAV model. The proposed scheme has been shown to be robust to system and measurement noise and sensitive to a wide range of fault types. In the second part of this thesis, a FADS system is designed and tested on a mini air vehicle (MAV). With the primary goal of most air vehicle manufacturers being the reduction of costs, researchers found the concept of air data measurements using a matrix of pressure orifices to be a cheaper alternative to the standard air data boom. The concept of FADS systems is not new and has been quite popular in several NASA projects. However few applications consider MAVs where weight and cost implications can restrict the use of air data booms. The FADS system designed in this thesis has been shown to produce accurate air data estimations but more importantly has reduced instrumentation weight and cost by almost 80% and 97% respectively in comparison to a standard air data boom. The conclusions drawn from this thesis are clearly outlined at the end of each chapter and future work is also brought together in the final chapter.