Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle, a dynamic particle, and a dynamic rigid body models are developed to measure wind velocity in hovering flight. Wind tunnel and steady level flight tests are used to characterize kinematic and dynamic particle models. System identification stepwise regression and output error algorithms are used to determine the model structure and parameter estimates of rigid body models. The comparison of all three models demonstrates the rigid body model to have higher performance resolving slow-varying winds based on a frequency response analysis and field experiments conducted next to a 3-D sonic anemometer. The dissertation also presents an extension of the rigid body wind estimation framework to profile the horizontal components of wind velocity in vertical steady ascending flight. The extension employed system identification to characterize five rigid body models for steady-ascending flight speeds increasing from 0 to 2 m/s in intervals of 0.5~m/s. State observers for wind profiling were synthesized using all five rigid body models. Performance assessments employing wind observations from in situ and remote sensors demonstrated model-based wind profiling results to be be in close agreement with ground-truth wind observations. Finally, the rigid body wind sensing framework developed in this dissertations for multirotor UAS is employed to support science objectives for the Advanced Lagrangian Predictions for Hazards Assessment Project. Quadrotor wind measurements sampled at 10 m above sea level were used to characterize the leeway of a person in water for search and rescue scenarios. Leeway values determined from quadrotor wind measurements were found to be in close to leeway parameters previous published in the literature. This results demonstrates the utility of model-based wind sensing for multirotor UAS for providing wind velocity observations in complex environments where conventional wind observations are not readily available. / Doctor of Philosophy / Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle model, a dynamic particle model, and a dynamic rigid body model, are characterized to measure wind velocity in hovering flight. Parameter characterizations are realized using data from wind tunnel, steady level flight tests and system identification experiments. Model-based wind estimations algorithms are developed using the kinematic particle model directly and by synthesizing state observers for the dynamic particle and rigid body models separately. For comparison purposes, the frequency response characteristic of the dynamic particle and rigid body models is examined to determine the range of wind fluctuations that each model can resolve. Performance comparisons demonstrate that the rigid body model to resolve higher wind fluctuations and yield more accurate wind estimates. The dissertation extends the rigid body wind estimation algorithm to estimate wind velocity profiles of the horizontal wind vector. The rigid body wind estimation algorithms is used to answer science questions about about the drift of a person in water.

Energy-shaping techniques are used to expand the range of autonomous motion of unmanned aerial systems without prohibitively {color{black}increasing the computational cost of the resultant controller}. Passivity-based control presents a method to implement a static, nonlinear state feedback control law that stabilizes the motion of an aircraft with a large region of attraction. {color{black} The energy-based control scheme is applied to both multirotor and fixed-wing aircraft}. Multirotor aircraft dynamics are cast into a port-Hamiltonian System and the concept of trajectory tracking using canonical feedback transformation is implemented to construct a cross-track controller. Fixed-wing aircraft dynamics are cast in port-Hamiltonian form and a passivity-based nonlinear control law for steady, wings-level flight of a fixed-wing aircraft to a specified inertial velocity (speed, course, and climb angle) is constructed. Results in simulations and experiments suggest robustness, and a large region of attraction of the controller. The control law extended to support time-varying inertial velocity tracking that incorporates banking to turn. The results are extended by including a line-of-sight guidance law and varying the direction as a function of position relative to a desired path, rather than as a function of time. The control law and the associated proof of stability follow similarly to that of the time-varying directional stabilization problem. The results are supported with simulations as well as experimental flight tests. / Doctor of Philosophy / This dissertation presents an alternative but intuitive approach to regulate unmanned aerial vehicles' flight that would allow for more maneuverability {color{black} than conventional methods}. This scheme relies on modifying the energy of the system to achieve the desired motion and leverages the properties of the aircraft rather than eliminating them and imposing different properties. This approach is applied to both fixed-wing and aircraft and quadcopters. Simulations and experimental flights have show the efficacy of this approach compared to other more established methods.

The current proposed unmanned aircraft system (UAS) detect and avoid standards require the same safety metrics, even when in close proximity to the ground or structures. This requirement has the potential to hinder low altitude small unmanned aircraft operations, such as local package delivery and utility inspection. One of the main safety metrics for UASs to adhere to is a ``well clear" volume that quantifies the vertical and horizontal separation UASs are required to maintain from manned aircraft. The current volume of 2000 feet horizontal and +/- 250 feet vertical does not provide credit for the safety benefit of being close to an obstacle where manned aircraft do not fly and could prove to be too restricting for low-level flight operations (i.e., under 400 feet above ground level). This thesis suggests using smaller safety metric volumes than the well clear volume to demonstrate that operations at lower altitudes can still be proven to be just as safe as if they were held to the larger well clear volume standard by using obstacle and terrain shielding. The research leverages simulation to analyze different safety metrics and provides an example use case in which the methodology of shielded operations is applied to demonstrate how this methodology can be applied for a safety case. / Master of Science / With the development of small unmanned aircraft system (sUAS) technologies have come many practical and regulatory challenges, especially in low altitude airspaces. At lower altitudes, manned aircraft are likely to be operating at lower velocities and restricting standards require UASs to maneuver against aircraft that may not present a significant risk of collision. The excessive avoidance maneuvering can cause the successful execution of even simple operations such as package delivery or survey operations to become difficult. The strict requirements have the potential to specifically inhibit sUAS beyond visual line-of-sight commercial operations, which are of great interest to the industry. This thesis describes a method for demonstrating an equivalent level of safety of small UAS operations when utilizing avoidance algorithms that leverage obstacle and terrain awareness. The purpose of this research is to demonstrate that by remaining close to obstacles, which pose a hazard to other aircraft, an unmanned aircraft can lower the risk of a mid-air collision and to demonstrate an equivalent level of safety for operations using a reduced safety metrics.

The operation of sophisticated unmanned aircraft systems (UAS) involves complex interactions between human and machine. Unlike other areas of aviation where technological advancement has flourished to accommodate the modernization of the National Airspace System (NAS), the scientific paradigm of UAS and UAS user interface design has received little research attention and minimal effort has been made to aggregate accurate data to assess the effectiveness of current UAS human-machine interface (HMI) representations for command and control. UAS HMI usability is a primary human factors concern as the Federal Aviation Administration (FAA) moves forward with the full-scale integration of UAS in the NAS by 2025. This study examined system learnability of an industry standard UAS HMI as minimal usability data exists to support the state-of-the art for new and innovative command and control user interface designs. This study collected data as it pertained to the three classes of objective usability measures as prescribed by the ISO 9241-11. The three classes included: (1) effectiveness, (2) efficiency, and (3) satisfaction. Data collected for the dependent variables incorporated methods of video and audio recordings, a time stamped simulator data log, and the SUS survey instrument on forty-five participants with none to varying levels of conventional flight experience (i.e., private pilot and commercial pilot). The results of the study suggested that those individuals with a high level of conventional flight experience (i.e., commercial pilot certificate) performed most effectively when compared to participants with low pilot or no pilot experience. The one-way analysis of variance (ANOVA) computations for completion rates revealed statistical significance for trial three between subjects [F (2, 42) = 3.98, p = 0.02]. Post hoc t-test using a Bonferroni correction revealed statistical significance in completion rates [t (28) = -2.92, p<0.01] between the low pilot experience group (M = 40%, SD =. 50) and high experience group (M = 86%, SD = .39). An evaluation of error rates in parallel with the completion rates for trial three also indicated that the high pilot experience group committed less errors (M = 2.44, SD = 3.9) during their third iteration when compared to the low pilot experience group (M = 9.53, SD = 12.63) for the same trial iteration. Overall, the high pilot experience group (M = 86%, SD = .39) performed better than both the no pilot experience group (M = 66%, SD = .48) and low pilot experience group (M = 40%, SD =.50) with regard to task success and the number of errors committed. Data collected using the SUS measured an overall composite SUS score (M = 67.3, SD = 21.0) for the representative HMI. The subscale scores for usability and learnability were 69.0 and 60.8, respectively. This study addressed a critical need for future research in the domain of UAS user interface designs and operator requirements as the industry is experiencing revolutionary growth at a very rapid rate. The deficiency in legislation to guide the scientific paradigm of UAS has generated significant discord within the industry leaving many facets associated with the teleportation of these systems in dire need of research attention. Recommendations for future work included a need to: (1) establish comprehensive guidelines and standards for airworthiness certification for the design and development of UAS and UAS HMI for command and control, (2) establish comprehensive guidelines to classify the complexity associated with UAS systems design, (3) investigate mechanisms to develop comprehensive guidelines and regulations to guide UAS operator training, (4) develop methods to optimize UAS interface design through automation integration and adaptive display technologies, and (5) adopt methods and metrics to evaluate human-machine interface related to UAS applications for system usability and system learnability.

The theory of Lagrangian Coherent Structures (LCS) enables prediction of material transport by turbulent winds, such as those observed in the Earth's Atmospheric Boundary Layer. In this dissertation, both theory and experimental methods are developed for utilizing small fixed-wing unmanned aircraft systems (UAS) in detecting these atmospheric coherent structures. The dissertation begins by presenting relevant literature on both LCS and airborne wind estimation. Because model-based wind estimation inherently depends on high quality models, a Flight Dynamic Model (FDM) suitable for a small fixed-wing aircraft in turbulent wind is derived in detail. In this presentation, some new theoretical concepts are introduced concerning the proper treatment of spatial wind gradients, and a critical review of existing theories is presented. To enable model-based wind estimation experiments, an experimental approach is detailed for identifying a FDM for a small UAS by combining existing computational aerodynamic and data-driven approaches. Additionally, a methodology for determining wind estimation error directly resulting from dynamic modeling choices is presented and demonstrated. Next, some model-based wind estimation results are presented utilizing the experimentally identified FDM, accompanied by a discussion of model fidelity concerns and other experimental issues. Finally, an algorithm for detecting LCS from a single circling fixed-wing UAS is developed and demonstrated in an Observing System Simulation Experiment. The dissertation concludes by summarizing these contributions and recommending future paths for continuing research. / Doctor of Philosophy / In a natural or man-made disaster, first responders depend on accurate predictions of where the wind might carry hazardous material. A mathematical theory of Lagrangian Coherent Structures (LCS) has shown promise in ocean environments to improve these predictions, and the theory is also applicable to atmospheric flows near the Earth’s surface. This dissertation presents both theoretical and experimental research efforts towards employing small fixed-wing unmanned aircraft systems (UAS) to detect coherent structures in the Atmospheric Boundary Layer (ABL). These UAS fit several “gaps” in available sensing technology: a small aircraft responds significantly to wind gusts, can be steered to regions of interest, and can be flown in dangerous environments without risking the pilot’s safety. A key focus of this dissertation is to improve the quality of airborne wind measurements provided by inexpensive UAS, specifically by leveraging mathematical models of the aircraft. The dissertation opens by presenting the motivation for this research and existing literature on the topics. Next, a detailed derivation of a suitable Flight Dynamic Model (FDM) for a fixed-wing aircraft in a turbulent wind field is presented. Special attention is paid to the theories for including aerodynamic effects of flying in non-uniform winds. In preparation for wind measurement experiments, a practical method for obtaining better quality FDMs is presented which combines theoretically based and data-driven approaches. A study into the wind-measurement error incurred solely by mathematical modeling is presented, focusing on simplified forms of the FDM which are common in aerospace engineering. Wind estimates which utilize our best available model are presented, accompanied by discussions of the model accuracy and additional wind measurement concerns. A method is developed to detect coherent structures from a circling UAS which is providing wind information, presumably via accurate model based estimation. The dissertation concludes by discussing these conclusions and directions for future research which have been identified during these pursuits.

The main contributions of this work are applications of robust control and analysis methods to complex engineering systems, namely, small fixed-wing unmanned aircraft systems (UAS). Multiple path-following controllers for a small fixed-wing Telemaster UAS are presented, including a linear parameter-varying (LPV) controller scheduled over path curvature. The controllers are synthesized based on a lumped path-following and UAS dynamic system, effectively combining the six degree-of-freedom aircraft dynamics with established parallel transport frame virtual vehicle dynamics. The robustness and performance of these controllers are tested in a rigorous MATLAB simulation environment that includes steady winds, turbulence, measurement noise, and delays. After being synthesized off-line, the controllers allow the aircraft to follow prescribed geometrically defined paths bounded by a maximum curvature. The controllers presented within are found to be robust to the disturbances and uncertainties in the simulation environment. A robust analysis framework for mathematical validation of flight control systems is also presented. The framework is specifically developed for the complete uncertainty characterization, quantification, and analysis of small fixed-wing UAS. The analytical approach presented within is based on integral quadratic constraint (IQC) analysis methods and uses linear fractional transformations (LFTs) on uncertainties to represent system models. The IQC approach can handle a wide range of uncertainties, including static and dynamic, linear time-invariant and linear time-varying perturbations. While IQC-based uncertainty analysis has a sound theoretical foundation, it has thus far mostly been applied to academic examples, and there are major challenges when it comes to applying this approach to complex engineering systems, such as UAS. The difficulty mainly lies in appropriately characterizing and quantifying the uncertainties such that the resulting uncertain model is representative of the physical system without being overly conservative, and the associated computational problem is tractable. These challenges are addressed by applying IQC-based analysis tools to analyze the robustness of the Telemaster UAS flight control system. Specifically, uncertainties are characterized and quantified based on mathematical models and flight test data obtained in house for the Telemaster platform and custom autopilot. IQC-based analysis is performed on several time-invariant H∞ controllers along with various sets of uncertainties aimed at providing valuable information for use in controller analysis, controller synthesis, and comparison of multiple controllers. The proposed framework is also transferable to other fixed-wing UAS platforms, effectively taking IQC-based analysis beyond academic examples to practical application in UAS control design and airworthiness certification. IQC-based analysis problems are traditionally solved using convex optimization techniques, which can be slow and memory intensive for large problems. An oracle for discrete-time IQC analysis problems is presented to facilitate the use of a cutting plane algorithm in lieu of convex optimization in order to solve large uncertainty analysis problems relatively quickly, and with reasonable computational effort. The oracle is reformulated to a skew-Hamiltonian/Hamiltonian eigenvalue problem in order to improve the robustness of eigenvalue calculations by eliminating unnecessary matrix multiplications and inverses. Furthermore, fast, structure exploiting eigensolvers can be employed with the skew-Hamiltonian/Hamiltonian oracle to accurately determine critical frequencies when solving IQC problems. Applicable solution algorithms utilizing the IQC oracle are briefly presented, and an example shows that these algorithms can solve large problems significantly faster than convex optimization techniques. Finally, a large complex engineering system is analyzed using the oracle and a cutting-plane algorithm. Analysis of the same system using the same computer hardware failed when employing convex optimization techniques. / Ph. D.

<p>Due to their rapid technological advancement, mobile sensors such as unmanned aerial vehicles (UAVs) are seeing growing application in the area of multi-target tracking and identity management (MTIM). For efficient and sustainable performance of a MTIM system with mobile sensors, proper algorithms are needed to both effectively estimate the states/identities of targets from sensing data and optimally guide the mobile sensors based on the target estimates. One major challenge in MTIM is that a target may be temporarily lost due to line-of-sight breaks or corrupted sensing data in cluttered environments. It is desired that these targets are kept tracking and identification, especially when they reappear after the temporary loss of detection. Another challenging task in MTIM is to correctly track and identify targets during track coalescence, where multiple targets get close to each other and could be hardly distinguishable. In addition, while the number of targets in the sensors’ surveillance region is usually unknown and time-varying in practice, many existing MTIM algorithms assume their number of targets to be known and constant, thus those algorithms could not be directly applied to real scenarios.</p> <p>In this research, a set of solutions is developed to address three particular issues in MTIM that involves the above challenges: 1) using a single mobile sensor with a limited sensing range to track multiple targets, where the targets may occasionally lose detection; 2) using a network of mobile sensors to actively seek and identify targets to improve the accuracy of multi-target identity management; and 3) tracking and managing the identities of an unknown and time-varying number of targets in clutter.</p>

Safety is the top priority for the aviation industry and a safe airport environment is essential to aviation safety. However, due to the increasing prevalence of UAS in recent years, UAS sightings have become a potential threat to airports. When UAS appear in the vicinity of airports, they bring safety concerns and result in negative operational and economic impacts on airports. Since the FAA’s mission is to provide the safest and most efficient aerospace system in the world, further research regarding the threat of UAS sightings to airports is needed. The purpose of this study is to investigate the threat of UAS to airports and in the national airspace system (NAS). This study includes three primary components: the analysis of 6,551 Federal Aviation Administration (FAA) UAS sighting reports, a case study of the impacts of the UAS sighting at Newark Liberty International Airport (EWR) on January 22, 2019, and a synthesis of airport operator perspectives based on interviews with airport personnel at five airports. The analysis of UAS sighting reports shows the characteristics of UAS sightings, the case study on EWR UAS illustrates the impact of the UAS sighting at the airport, and interview results illustrate the current perspective of airport operators regarding the risk of UAS. Along with the results, the scientific methods of identifying and analyzing the characteristics of UAS sightings in controlled airspace close to airports could be used by researchers to study UAS sightings in the future. Findings from this study may be beneficial to multiple stakeholders, including airport personnel, regulators, entrepreneurs, and vendors in the aviation industry. <br>

<p>Due to the rapid development of technologies for unmanned aircraft systems (UAS's), the supply and demand market for UAS's is expanding globally. With the great number of UAS's ready to fly in civilian airspace, an UAS aircraft traffic management system that can guarantee the safe, resilient and efficient operation of UAS's is absent. The vast majority of existing literature on UAS traffic lacks of the attention to the fundamental characteristics of UAS operation, which leads to models and methods that are difficult to implement or lacks scalability. Motivated by these challenges, this research aims at achieving three objectives: 1) the proper frameworks that scale well with high-frequency, high-density UAS operations, 2) the models that captures the fundamental characteristics of UAS operations, 3) the methods that can be implemented in practice with guarantees of efficiency, safety, and resilience. In particular, the objectives are studied at low-level UAS traffic congestion control, agent-level UAS configuration control and unknown agent prediction. The proposed frameworks and obtained results offer comprehensive and practical guidelines of real world UAS operations at different levels.</p>

Small unmanned aircraft systems (sUAS) will become a disruptive force on the modern battlefield. In recent years, sUAS size and cost have decreased while their capability has increased. They have forced a reconsideration of the air superiority paradigm held since the First World War. Perhaps their most attractive, and worrisome, feature is the huge range of combat roles that they might fulfill. The presence of sUAS on future battlefields is certain, but the role they will play and their impact on those battlefields are not. This work presents a decision support framework for sUAS deployment in small infantry units. The framework is designed to explore and evaluate multiple sUAS-small-unit deployment concepts' impact on small unit effectiveness in a combat scenario of interest. The framework helps decision makers identify high-level sUAS deployment principles for testing and validation in physical experiments before sUAS are implemented on the battlefield. The decision support framework comprises the following: 1) a definition of the sUAS-small-unit deployment concept design space and combat scenario, 2) an agent-based computer model for exploring sUAS deployment concepts, 3) a set of analysis tools for evaluating sUAS deployment impact on combat effectiveness, and 4) suggestions for synthesizing high-level sUAS deployment principles from the analysis. In this work, the decision support framework for sUAS-small-unit deployment is used to explore and evaluate the impact of deploying an infantry platoon with between one and nine unmanned aerial vehicles (UAV) operating in a reconnaissance role while executing one of several sUAS patrol pattern variants. In a scenario in which a defending platoon uses sUAS to intercept and aid in indirect fires targeting against a platoon of attacking infantry, the sUAS were shown to markedly improve the defending platoon's combat effectiveness. The framework is used to synthesize several key principles for sUAS deployment in the scenario. It shows that, when fewer UAVs are deployed, short-range sUAS patrols improve defender combat effectiveness. Conversely, when more UAVs are deployed, long-range sUAS patrols improve the defenders' ability to target attacking units with indirect fires, increasing the firepower concentrated against opponents. The analysis also shows that increasing the number of deployed UAVs improves the likelihood of defending warfighters surviving the engagement and the defenders' ability to detect and engage the attackers with indirect fires. Finally, the framework shows that sUAS can force alterations in attacker behavior, removing them from combat by non-violent, but highly effective, means.