4DT generator and guidance system

This thesis describes a 4D Trajectories Generator and Guidance system. 4D trajectory is a concept that will improve the capacity, efficiency and safety of airspace. First a 4D trajectories synthetizer design is proposed. A flight plan composed by a set of waypoints, aircraft dynamics model and a set of limits and constraints are assembled into an optimal control problem. Optimal solution is found by making use of an optimal control solver which uses pseudo spectral parametrization together with a generic nonlinear programming solver. A 4D Trajectories generator is implemented as a stand-alone application and combined with a graphic user interface to give rise to 4D Trajectories Research Software (4DT RS) capable to generate, compare and test optimal trajectories. A basic Tracking & Guidance system with proportional navigation concept is developed. The system is implemented as a complementary module for the 4D trajectories research software. Simulation tests have been carried out to demonstrate the functionalities and capabilities of the 4DT RS software and guidance system. Tests cases are based on fuel and time optimization on a high-traffic commercial route. A standard departure procedure is optimized in order to reduce the noise perceived by village’s population situated near airport. The tracking & guidance module is tested with a commercial flight simulator for demonstrating the performance of the optimal trajectories generated by the 4DT RS software

Time-to-Contact-Based Control Laws for Flare Trajectory Generation and Landing Point Tracking in Autorotation

For many rotorcraft platforms, incorrect timing of the autorotation flare and deceleration maneuvers may result in significant aircraft damage and injury to the crew, or worse. There is a clear need for new pilot cueing and control augmentation technologies that lead to a higher probability of a successful autorotation landing. This paper describes a recent effort to develop two different Tau (time-to-contact)-based autorotation controllers from two different approaches that can be used to drive visual aids to help guide a pilot to apply the required control inputs to complete a safe autorotative landing

Enhanced air traffic flow and capacity management under trajectory based operations considering traffic complexity

Tesi amb menció internacional.(English) The Air Traffic Flow and Capacity Management (ATFCM) aims at maintaining the forecast traffic demand below the estimated capacity in airports and airspace sectors. The purpose is to maintain the workload of the air traffic controllers under safe limits and avoid overloaded situations. At present, the demand and the capacity management initiatives are deployed separately. Given a forecast traffic demand, the different air navigation service providers allocate their air traffic control resources providing the airspace sectorisations. Then, the network manager addresses the remaining overloads by allocating delay using the CASA algorithm based on a ration-by-schedule principle. It should be noted that some ad-hoc flights might be re-rerouted or limited in cruise altitude in order to avoid congested airspace by submitting a new flight plan. Hence, the previously chosen sectorisations may be not optimum once the demand management initiatives are deployed. Moreover, the flexibility of the airspace users is limited since they cannot express their preferences. Furthermore, the demand and the capacity are currently measured using entry counts as proxy of the air traffic control workload, which is rather easy to measure or estimate. Yet, this metric cannot evaluate the difficulty to handle different traffic patterns inside the sectors leading to the use of capacity buffers. This PhD focuses on overcoming the limitations of the current ATFCM system outlined before by the introduction of complexity metrics (instead of entry counts) in order to measure the traffic load, the better consideration of the airspace users preferences allowing the possibility of submitting alternative trajectories to avoid congested airspace, and the holistic integration of the demand and capacity management into the same optimisation problem. First, the integration of two capacity management initiatives, i.e. Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA), is studied proving some benefits when such integration is dynamic. Next, a new concept of operation is proposed where the airspace users have the option of submitting alternative trajectories and the network manager is the responsible for the demand management (delay allocation and choice of the used trajectory) and the capacity management (selection of the airspace sectorisation), considering a network-wide optimisation. This concept of operations is mathematically modelled with two Demand and Capacity Balancing (DCB) models addressing only demand management and three holistic DCB models where the demand and the capacity management measures are considered together in the same optimisation problem. A first model aims at choosing the best trajectory and delay allocation per flight while analysing the traffic load with entry counts at traffic volume level. It is solved in a realistic case study using the historical regulations providing a 76.84% of reduction in the arrival delay if compared to the current system.(Català) La gestió dels fluxos de trànsit i de la capacitat (ATFCM) té com a objectiu mantenir la demanda de trànsit prevista per sota de la capacitat estimada dels aeroports i els sectors de l’espai aeri. Actualment, les iniciatives de gestió de la demanda i de gestió de la capacitat es duen a terme separadament. Donada una previsió de trànsit, els diferents proveïdors de serveis de navegació aèria assignen els seus recursos proporcionant les sectoritzacions de l’espai aeri. Després l’administrador de la xarxa tracta les sobrecàrregues restants mitjançant l’assignació de retards utilitzant l'algoritme CASA, basat en l'ordenació per ordre d’arribada. A alguns vols també se’ls pot canviar la ruta o se’ls pot restringit l’altitud del creuer per tal d’evitar zones congestionades requerint la presentació d’un nou pla de vol. Així doncs, les sectoritzacions prèviament escollides poden ser no òptimes una vegada s’implementin les iniciatives de gestió de la demanda. A més, la flexibilitat dels usuaris de l’espai aeri és limitada ja que no poden expressar les seves preferències. Altrament, la demanda i la capacitat es mesuren actualment comptant el nombre d’arribades com a proxy de la càrrega de treball del control del trànsit aeri. No obstant això, aquesta mètrica no pot evaluar la dificultat de gestionar diferents patrons de trànsit dins els sectors, la qual cosa condueix a la utilització de marges de capacitat. Aquest PhD es centra en superar les limitacions de l’actual sistema d’ATFCM indicades anteriorment mitjançant la introducció de mètrics de complexitat (en lloc del número d’arribades) per a mesurar el trànsit, la millor consideració de les preferències dels usuaris de l’espai aeri permetent la possibilitat d’utilitzar trajectories alternatives per a evitar la congestió de l’espai aeri, i la integració holística de la gestió de la demanda i de la capacitat en el mateix problema d’optimització. Primer, s’estudia la integració de dues iniciatives de gestió de la capacitat: DAC i FCA. S’obtenen beneficis quan la integració és dinàmica. Després, es proposa un nou concepte operacional on els usuaris de l’espai aeri tenen l'opció de proposar trajectories alternatives i l’administrador de la xarxa és el responsable de la gestió de la demanda (assignació de retards i elecció de la trajectòria utilitzada) i de la capacitat (selecció de la sectorització de l’espai aeri) considerant l’optimització de tota la xarxa. Aquest concepte operacional es formula amb dos models de DCB que aborden només la gestió de la demanda i tres models holístics on la gestió de la demanda i de la capacitat es consideren conjuntament en el mateix problema d’optimització. Un primer model es centra en escollir la millor trajectòria i assignació de retard per vol, mentre que el trànsit s'avalua mitjançant el número d’arribades als volums de trànsit. Es resol un cas d’estudi realista on s’utilitzen les regulacions històriques aconseguint un 76.84% menys de retard a l'arribada si es compara amb els sistema actual. Un dels tres models holístics de s’estudia en detall, en concret el que utilitza mètriques de complexitat i optimitza les sectoritzacions de l’espai aeri escollint entre un seguit de configuracions disponibles. Aquest model es tracta amb un nou mètode híbrid presentat en aquest PhD i que combina la simulació del recuit i la programació dinàmica. En un primer cas d'estudi, aquest nou mètode es compara amb el mètode exacte resolt amb Gurobi proporcionant un millor rendiment principalment quan la dificultat del problema augmenta. En un segon cas d’estudi es realitza un estudi de sensibilitat del paràmetre que modela una penalització per a diferents configuracions consecutives. Finalment, es resol un escenari a gran escala amb el mètode híbrid proporcionant un 74.01% menys de retard a l'arribada i un 28.47% menys en el cost de la sectorització resultant en comparació amb un escenari de referència que representa les millors condicions del sistema actual.(Español) La gestión de los flujos de tráfico y de la capacidad (ATFCM) pretende mantener la demanda de tráfico prevista por debajo de la capacidad estimada de los aeropuertos y los sectores del espacio aéreo. Actualmente, las iniciativas de gestión de la demanda y de la capacidad se implementan por separado. Ante una previsión de tráfico, los diferentes proveedores de servicios de navegación aérea asignan sus recursos proporcionando las sectorizaciones del espacio aéreo. Después, el administrador de la red trata las sobrecargas restantes mediante la asignación de retrasos utilizando el algoritmo CASA basado en un principio de ordenación por orden de llegada. A algunos vuelos también se les puede cambiar de ruta o limitar la altitud de crucero para evitar la congestión del espacio aéreo requiriendo de un nuevo plan de vuelo. Así pues, las sectorizaciones elegidas anteriormente pueden no ser óptimas una vez que se implementen las iniciativas de gestión de la demanda. Adicionalmente, la flexibilidad de los usuarios del espacio aéreo es limitada ya que no pueden expresar sus preferencias. Además, la demanda y la capacidad se miden actualmente contando el número de llegadas como proxy de la carga de trabajo del control del tráfico aéreo. Sin embargo, esta métrica no puede evaluar la dificultad de controlar diferentes patrones de tráfico dentro de los sectores lo que conduce al uso de márgenes de capacidad. Este PhD se centra en superar las limitaciones del sistema de ATFCM actual descritas anteriormente mediante la introducción de métricas de complejidad (en lugar del número de llegadas) para medir la carga de tráfico, la mejor consideración de las preferencias de los usuarios del espacio aéreo permitiendo la posibilidad de la presentación de trayectorias alternativas para evitar la congestión, y la integración holística de la gestión de la demanda y de la capacidad en un mismo problema de optimización. Primero, se estudia la integración de dos iniciativas de gestión de la capacidad, DAC y FCA, demostrando beneficios cuando dicha integración es dinámica. A continuación, se propone un nuevo concepto operacional donde los usuarios del espacio aéreo tienen la opción de presentar trayectorias alternativas y el administrador de la red es el responsable de la gestión de la demanda (asignación de retrasos y elección de la trayectoria utilizada) y la gestión de la capacidad (selección de la sectorización), considerando una optimización de toda la red. Este concepto operacional se modela con dos modelos de DCB que abordan sólo la gestión de la demanda y tres modelos holísticos donde las medidas de gestión de la demanda y de la capacidad se consideran conjuntamente en el mismo problema de optimización. Un primer modelo pretende elegir la mejor asignación de trayectoria y retraso por vuelo mientras se analiza la carga de tráfico con el número de llegadas a nivel de volumen de tráfico. Se resuelve un caso de estudio utilizando las regulaciones históricas proporcionando un 76.84% de reducción en el retraso en la llegada si se compara con el sistema actual. El model holístico que utiliza métricas de complejidad y optimiza las sectorizaciones del espacio aéreo escogiendo entre un conjunto de configuraciones disponibles se estudia en detalle. Este modelo se trata con un nuevo método híbrido basado en el recocido simulado y la programación dinámica. En un primer caso de estudio, se compara este nuevo método con el método exacto resuelto con Gurobi proporcionando un mejor rendimiento cuando aumenta la dificultad del problema. En un segundo caso de estudio se realiza un estudio de sensibilidad del parámetro que modela una penalización para diferentes configuraciones consecutivas. Finalmente, se resuelve un escenario a gran escala con el método Híbrido proporcionando menores valores de retraso en llegada y menores costes en la sectorización resultante en comparación con un escenario de referencia que representa las mejores condiciones del sistema actual.Postprint (published version

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Fuzzy PD control of an optically guided long reach robot

This thesis describes the investigation and development of a fuzzy controller for a manipulator with a single flexible link. The novelty of this research is due to the fact that the controller devised is suitable for flexible link manipulators with a round cross section. Previous research has concentrated on control of flexible slender structures that are relatively easier to model as the vibration effects of torsion can be ignored. Further novelty arises due to the fact that this is the first instance of the application of fuzzy control in the optical Tip Feedback Sensor (TFS) based configuration. A design methodology has been investigated to develop a fuzzy controller suitable for application in a safety critical environment such as the nuclear industry. This methodology provides justification for all the parameters of the fuzzy controller including membership fUllctions, inference and defuzzification techniques and the operators used in the algorithm. Using the novel modified phase plane method investigated in this thesis, it is shown that the derivation of complete, consistent and non-interactive rules can be achieved. This methodology was successfully applied to the derivation of fuzzy rules even when the arm was subjected to different payloads. The design approach, that targeted real-time embedded control applicat.ions from the outset, results in a controller implementation that is suitable for cheaper CPU constrained and memory challenged embedded processors. The controller comprises of a fuzzy supervisor that is used to alter the derivative term of a linear classical Proportional + Derivative (PD) controller. The derivative term is updated in relation to the measured tip error and its derivative obtained through the TFS based configuration. It is shown that by adding 'intelligence' to the control loop in this way, the performance envelope of the classical controller can be enhanced. A 128% increase in payload, 73.5% faster settling time and a reduction of steady state of over 50% is achieved using fuzzy control over its classical counterpart

On scaling and system identification of flexible aircraft dynamics.

The use of subscale models has been common practice in the industry and has helped engineers gain more confidence in their design processes. However, each subscale model is developed for a specifc test, and consequently, different types of models are needed for observing aerodynamic, structural and aeroelastic characteristics of a full-scale aircraft. Yet, traditional aircraft design methods face serious challenges when a novel aircraft de- sign emerges and a proof-of-concept is needed for investigating this multi-disciplinary problem. An example of such a problem is the development of aircraft configurations with high aspect ratio wings for which the disciplines of aeroelastic and flight mechanics are strongly interconnected. Moreover, if the prediction of dynamic behaviour is of interest, a method that utilises system identification for analysing experimental data is of importance. Therefore, this thesis aims to develop a methodology to investigate the complex flight dynamic behaviour of flexible aircraft by combining techniques for developing subscale models and methods with the field of system identification. This aim is achieved through three objectives: 1) assessment of system identification methods for subscale flexible aircraft, 2) theoretical development of subscale modelling in terms of scaling laws and aeroelastic simulation framework and, 3) wind tunnel testing of the subscale model. Aspects of System Identification have been explored through use-cases where experimental data for a rigid aircraft both in full-scale and subscale configuration is used. The results highlight the fact that in testing a subscale model, dynamics are more prone to exhibit non-linear behaviour when compared to the full-scale model. It followed by the application of system identification for a flexible aircraft based on a simulation framework. This study emphasised the need for non-linear identification methods, such as an output error method, to characterise a flexible aircraft system. The work continues with the exploration of scaling laws applied to a simple aerofoil that is free to pitch and plunge. These results build the foundation for the development of a subscale high aspect ratio wing for wind tunnel experiments. The work highlights the trade-o s and compromises faced during the development of a dynamically subscaled model and the practice of system identification. The main contribution lies in the development of a low-cost methodology in building a subscale model that allows the use of dynamically scaled models at the early design stages. This practice provides the designer with a means to de-risk novel aircraft concepts as early as possible and in doing so, reduce overall development costs.PhD in Aerospac

Self–organised multi agent system for search and rescue operations

Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore an unknown search terrain with complex topology in multiple predefined stages by performing pertinent strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined environments is one of the main challenges for search and rescue robots inside collapsed buildings. In this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform a fast, fair, and thorough search as well as solving the multi–agent traffic congestion. Our simulations have been performed on different test environments in which the complexity of the search field has been defined by fractal dimension of Brownian movements. The exploration stages are depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research is on the red arena with the least structure and most challenging parts to robot nimbleness