Fault detection, identification and accommodation techniques for unmanned airborne vehicles

Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures

Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells

With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100\% for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption.Comment: Accepted at IEEE WoWMoM 2017 - 9 pages, 2 tables, 4 figure

How much does a man cost? A dirty, dull, and dangerous application

Thesis (M.A.) University of Alaska Fairbanks, 2017This study illuminates the many abilities of Unmanned Aerial Vehicles (UAVs). One area of importance includes the UAV's capability to assist in the development, implementation, and execution of crisis management. This research focuses on UAV uses in pre and post crisis planning and accomplishments. The accompaniment of unmanned vehicles with base teams can make crisis management plans more reliable for the general public and teams faced with tasks such as search and rescue and firefighting. In the fight for mass acceptance of UAV integration, knowledge and attitude inventories were collected and analyzed. Methodology includes mixed method research collected by interviews and questionnaires available to experts and ground teams in the UAV fields, mining industry, firefighting and police force career field, and general city planning crisis management members. This information was compiled to assist professionals in creation of general guidelines and recommendations for how to utilize UAVs in crisis management planning and implementation as well as integration of UAVs into the educational system. The results from this study show the benefits and disadvantages of strategically giving UAVs a role in the construction and implementation of crisis management plans and other areas of interest. The results also show that the general public is lacking information and education on the abilities of UAVs. This education gap shows a correlation with negative attitudes towards UAVs. Educational programs to teach the public benefits of UAV integration should be implemented

Multimodal hybrid powerplant for unmanned aerial systems (UAS) robotics

Most UAS propulsion systems currently utilize either Internal Combustion Engines (ICE) or Electric Motor (EM) prime movers. ICE are favoured for aircraft use due to the superior energy density of fuel compared to batteries required for EM, however EM have several significant advantages. A major advantage of EM is that they are inherently self starting have predictable response characteristics and well developed electronic control systems. EMs are thus very easy to adapt to automatic control, whereas ICE have more complex control response and an auxiliary starting motor is required for automated starting. This paper presents a technique for determining the performance, feasibility and effectiveness of powerplant hybridisation for small UAS. A Hybrid Powerplant offers the possibility of a radical improvement in the autonomy of the aircraft for various tasks without sacrificing payload range or endurance capability. In this work a prototype Aircraft Hybrid Powerplant (AHP) was designed, constructed and tested. It is shown that an additional 35% continuous thrust power can be supplied from the hybrid system with an overall weight penalty of 5%, for a given UAS. Dynamometer and windtunnel results were obtained to validate theoretical propulsion load curves. Using measured powerplant data and an assumed baseline airframe performance characteristic, theoretical endurance comparisons between hybrid and non-hybrid powerplants were determined. A flight dynamic model for the AHP was developed and validated for the purposes of operational scenario analysis. Through this simulation it is shown that climb rates can be improved by 56% and endurance increased by 13%. The advantages of implementing a hybrid powerplant have been baselined in terms of payload range and endurance. Having satisfied these parameters, a whole new set of operational possibilities arises which cannot be performed by non-self-starting ICE only powered aircraft. A variety of autonomous robotic aircraft tasks enabled by the hybrid powerplant is discussed