Attitude Estimation and Control Using Linear-Like Complementary Filters: Theory and Experiment

This paper proposes new algorithms for attitude estimation and control based on fused inertial vector measurements using linear complementary filters principle. First, n-order direct and passive complementary filters combined with TRIAD algorithm are proposed to give attitude estimation solutions. These solutions which are efficient with respect to noise include the gyro bias estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude tracking based on inertial vector measurements. Thus, instead of using noisy raw measurements in the control law a new solution of control that includes a linear-like complementary filter to deal with the noise is proposed. The stability analysis of the tracking error dynamics based on LaSalle's invariance theorem proved that almost all trajectories converge asymptotically to the desired equilibrium. Experimental results, obtained with DIY Quad equipped with the APM2.6 auto-pilot, show the effectiveness and the performance of the proposed solutions.Comment: Submitted for Journal publication on March 09, 2015. Partial results related to this work have been presented in IEEE-ROBIO-201

Rigid Body Attitude Estimation: An Overview and Comparative Study

The attitude estimation of rigid body systems has attracted the attention of many researchers over the years. The development of efficient estimation algorithms that can accurately estimate the orientation of a rigid body is a crucial step towards a reliable implementation of control schemes for underwater and flying vehicles. The primary focus of this thesis consists in investigating various attitude estimation techniques and their applications. Two major classes are discussed. The first class consists of the earliest static attitude determination techniques relying solely on a set of body vector measurements of known vectors in the inertial frame. The second class consists of dynamic attitude estimation and filtering techniques, relying on body vector measurements as well other measurements, and using the dynamical equations of the system under consideration. Various attitude estimation algorithms, including the latest nonlinear attitude observers, are presented and discussed, providing a survey that covers the evolution and structural differences of these estimation methods. Simulation results have been carried out for a selected number of such attitude estimators. Their performance in the presence of noisy measurements, as well as their advantages and disadvantages are discussed

Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

On sensor fusion for airborne wind energy systems

A study on filtering aspects of airborne wind energy generators is presented. This class of renewable energy systems aims to convert the aerodynamic forces generated by tethered wings, flying in closed paths transverse to the wind flow, into electricity. The accurate reconstruction of the wing's position, velocity and heading is of fundamental importance for the automatic control of these kinds of systems. The difficulty of the estimation problem arises from the nonlinear dynamics, wide speed range, large accelerations and fast changes of direction that the wing experiences during operation. It is shown that the overall nonlinear system has a specific structure allowing its partitioning into sub-systems, hence leading to a series of simpler filtering problems. Different sensor setups are then considered, and the related sensor fusion algorithms are presented. The results of experimental tests carried out with a small-scale prototype and wings of different sizes are discussed. The designed filtering algorithms rely purely on kinematic laws, hence they are independent from features like wing area, aerodynamic efficiency, mass, etc. Therefore, the presented results are representative also of systems with larger size and different wing design, different number of tethers and/or rigid wings.Comment: This manuscript is a preprint of a paper accepted for publication on the IEEE Transactions on Control Systems Technology and is subject to IEEE Copyright. The copy of record is available at IEEEXplore library: http://ieeexplore.ieee.org

Shape, motion, and inertial parameter estimation of space objects using teams of cooperative vision sensors

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005."February 2005."Includes bibliographical references (leaves 133-140).Future space missions are expected to use autonomous robotic systems to carry out a growing number of tasks. These tasks may include the assembly, inspection, and maintenance of large space structures; the capture and servicing of satellites; and the redirection of space debris that threatens valuable spacecraft. Autonomous robotic systems will require substantial information about the targets with which they interact, including their motions, dynamic model parameters, and shape. However, this information is often not available a priori, and therefore must be estimated in orbit. This thesis develops a method for simultaneously estimating dynamic state, model parameters, and geometric shape of arbitrary space targets, using information gathered from range imaging sensors. The method exploits two key features of this application: (1) the dynamics of targets in space are highly deterministic and can be accurately modeled; and (2) several sensors will be available to provide information from multiple viewpoints. These features enable an estimator design that is not reliant on feature detection, model matching, optical flow, or other computation-intensive pixel-level calculations. It is therefore robust to the harsh lighting and sensing conditions found in space. Further, these features enable an estimator design that can be implemented in real- time on space-qualified hardware. The general solution approach consists of three parts that effectively decouple spatial- and time-domain estimations. The first part, referred to as kinematic data fusion, condenses detailed range images into coarse estimates of the target's high-level kinematics (position, attitude, etc.).(cont.) A Kalman filter uses the high-fidelity dynamic model to refine these estimates and extract the full dynamic state and model parameters of the target. With an accurate understanding of target motions, shape estimation reduces to the stochastic mapping of a static scene. This thesis develops the estimation architecture in the context of both rigid and flexible space targets. Simulations and experiments demonstrate the potential of the approach and its feasibility in practical systems.by Matthew D. Lichter.Ph.D

Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications

Mención Internacional en el título de doctorNavigation information is an essential element for the functioning of robotic platforms and intelligent transportation systems. Among the existing technologies, Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term for referring to a constellation of satellites which transmit radio signals used primarily for ranging information. Therefore, the successful operation and deployment of prospective autonomous systems is subject to our capabilities to support GNSS in the provision of robust and precise navigational estimates. GNSS signals enable two types of ranging observations: –code pseudorange, which is a measure of the time difference between the signal’s emission and reception at the satellite and receiver, respectively, scaled by the speed of light; –carrier phase pseudorange, which measures the beat of the carrier signal and the number of accumulated full carrier cycles. While code pseudoranges provides an unambiguous measure of the distance between satellites and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets, carrier phase measurements present a much higher precision, at the cost of being ambiguous by an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase observations which, in turn, lead to complicated estimation problems. This thesis deals with the estimation theory behind the provision of carrier phase-based precise navigation for vehicles traversing scenarios with harsh signal propagation conditions. Contributions to such a broad topic are made in three directions. First, the ultimate positioning performance is addressed, by proposing lower bounds on the signal processing realized at the receiver level and for the mixed real- and integer-valued problem related to carrier phase-based positioning. Second, multi-antenna configurations are considered for the computation of a vehicle’s orientation, introducing a new model for the joint position and attitude estimation problems and proposing new deterministic and recursive estimators based on Lie Theory. Finally, the framework of robust statistics is explored to propose new solutions to code- and carrier phase-based navigation, able to deal with outlying impulsive noises.La información de navegación es un elemental fundamental para el funcionamiento de sistemas de transporte inteligentes y plataformas robóticas. Entre las tecnologías existentes, los Sistemas Globales de Navegación por Satélite (GNSS) se han consolidado como la piedra angular para la navegación en exteriores, dando acceso a localización y sincronización temporal a una escala global, irrespectivamente de la condición meteorológica. GNSS es el término genérico que define una constelación de satélites que transmiten señales de radio, usadas primordinalmente para proporcionar información de distancia. Por lo tanto, la operatibilidad y funcionamiento de los futuros sistemas autónomos pende de nuestra capacidad para explotar GNSS y estimar soluciones de navegación robustas y precisas. Las señales GNSS permiten dos tipos de observaciones de alcance: –pseudorangos de código, que miden el tiempo transcurrido entre la emisión de las señales en los satélites y su acquisición en la tierra por parte de un receptor; –pseudorangos de fase de portadora, que miden la fase de la onda sinusoide que portan dichas señales y el número acumulado de ciclos completos. Los pseudorangos de código proporcionan una medida inequívoca de la distancia entre los satélites y el receptor, con una precisión de decímetros cuando no se tienen en cuenta los retrasos atmosféricos y los desfases del reloj. En contraposición, las observaciones de la portadora son super precisas, alcanzando el milímetro de exactidud, a expensas de ser ambiguas por un número entero y desconocido de ciclos. Por ende, el alcanzar la máxima precisión con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo cual implica unos problemas de estimación de elevada complejidad. Esta tesis versa sobre la teoría de estimación relacionada con la provisión de navegación precisa basada en la fase de la portadora, especialmente para vehículos que transitan escenarios donde las señales no se propagan fácilmente, como es el caso de las ciudades. Para ello, primero se aborda la máxima efectividad del problema de localización, proponiendo cotas inferiores para el procesamiento de la señal en el receptor y para el problema de estimación mixto (es decir, cuando las incógnitas pertenecen al espacio de números reales y enteros). En segundo lugar, se consideran las configuraciones multiantena para el cálculo de la orientación de un vehículo, presentando un nuevo modelo para la estimación conjunta de posición y rumbo, y proponiendo estimadores deterministas y recursivos basados en la teoría de Lie. Por último, se explora el marco de la estadística robusta para proporcionar nuevas soluciones de navegación precisa, capaces de hacer frente a los ruidos atípicos.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi

UAV perception for safe flight under physical interaction

Aplicat embargament des de la data de defensa fins al maig 2020The control of autonomous flying vehicles with navigation purposes is a challenging task. Complexity arises mainly due to the non-linearity and uncertainty inherently present in the flight mechanics and aircraft-air interactions. Recently, interest has grown for equipping unmanned vehicles with the capacity to interact with their environment, other vehicles or humans. This will enable interesting applications such as autonomous load carrying, aerial refueling or parcel delivering. Having measured the interaction wrenches ease the control problem which can be configured to reject disturbances or to take profit of them to fulfill mission objectives. This thesis will contribute to this area by providing perception solutions which use limited and low cost sensors that enable state and disturbance estimation for possible, but not restricted to, interaction scenarios. This thesis contain three parts. The first part, introduces basic concepts related to the navigation state, aircraft dynamics, and sensor models. In addition, the platform under study is presented and mathematical models associated to it are calibrated. The second part is devoted to the observability analysis and the design of state observers. Linear and non-linear observability analysis techniques are used to unveil that the state of quadrotors equipped with GPS, magnetometers an IMU sensors cannot be uniquely identified in some specific flight configurations. Results of this section are relevant because the conflicting flight configurations contain hover, a flight maneuverer central in many unmanned aerial missions of VtoL vehicles. For many possible singular configurations, insightful descriptions and interpretations of the solution space known as indistinguishable region is provided. Findings are verified in simulation scenarios where it can be seen how a filter fails to recover the true state of an aircraft when imposing the hover flight condition. We discuss then the design of Extended Kalman Filters for state estimation that considers the available sensors. Issues that are typically not reported in the literature, such as when to update or propagate in the estimator algorithm or which coordinate frame should be used to represent each state variable are discussed. This leads to the formulation of four potentially equivalent but different discrete event-based filters for which precise algorithmic expressions are given. We compare the results of the four filters in simulation under known favorable conditions for observability. In order to diminish the effect of flying in the conflicting observability configurations, we provide an alternative filter based on the Schmidt Kalman Filter (SKF). The proposed filter shares the structure of the EKF, behaves better in the instants that the EKF fails and provides similar results in the remaining conditions. The last part of the thesis deals with the estimation of external disturbances. Disturbance estimation results are based on the derivation of a linear model for the aircraft dynamics which then extended with a high order disturbance model to enable the estimation of fast varying disturbances. Two external disturbance estimators from the literature are reviewed and adapted to the new model. Also, two Kalman observers that exploit the linearity of the derived model are presented. A simulation comparison is provided demonstrating that the KF disturbance estimators outperform the other. In addition, this part presents a design methodology of generic quadratic bounded observers for linear systems with ellipsoidal bounded uncertainty. The derived observers maximize a user tunable compromise between the estimation convergence speed and the final volume containing the estimation error. An observer for disturbances acting on a flying platform is derived considering the high order disturbance model above mentioned. Finally, an analysis of the estimation performance with respect to the design parameters is presented.Esta tesis, contribuye en este área formulando soluciones de percepción que permiten la estimación del estado y perturbaciones externas en condiciones normales de vuelos así como casos de interacción para UAVs equipados con sensores limitados y de bajo coste. La tesis se estructura en tres partes. La primera de ellas introduce los conceptos básicos relacionados con el estado de navegación, la dinámica de la aeronave y modelos de sensores. Además, se presenta la plataforma de estudio así como los modelos matemáticos asociados a ella y su calibración. La segunda parte está destinada al análisis de observabilidad y el diseño de observadores de estado. Los resultados de esta sección son importantes porque dentro de las condiciones de vuelo conflictivas se encuentra el vuelo a punto fijo, una maniobra de vuelo central en muchas misiones de vehículos VToL. Se analizan estas condiciones críticas de vuelo y para ellas se deriva y describe el espacio de soluciones posible conocido como región indistinguible. Los resultados son verificados en simulación dónde se puede apreciar como un estimador de estado falla al intentar realizar su tarea cuando la aeronave está en vuelo a punto fijo. Seguidamente se presenta el diseño de filtros extendidos de Kalman (EKF) que proveen estimaciones del estado con la información limitada de los sensores disponibles. Se discuten conceptos que habitualmente no se presentan en la literatura como cuando actualizar o propagar en el algoritmo de estimación o que sistema de referencia se debe utilizar para representar adecuadamente las variables de estado. Esto lleva a la formulación algorítmica de cuatro filtros discretos basados en eventos, diferentes, pero en esencia equivalentes. Se derivan rutinas de inicialización para los filtros y se comparan los resultados en simulación bajo condiciones favorables de estimación. Con la idea de disminuir el efecto de volar en configuraciones de observabilidad conflictivas, se deriva un filtro alternativo basado en el filtro de Schmidt Kalman (SKF). El filtro propuesto comparte estructura con el EKF, tiene un mejor comportamiento allí dónde le EKF falla y una respuesta similar en el resto de condiciones de vuelo. La última parte de la tesis trata con la estimación de perturbaciones externas. Para ello se deriva un modelo lineal que relaciona fuerzas y momentos con velocidades junto a un modelo de alto orden para las perturbaciones. Se estudia su aplicación a dos modelos para la estimación de perturbaciones ya presentes en la literatura. Además, se proponen dos nuevos filtros de Kalman que se aprovechan de la linealidad del modelo. Se presenta una comparativa basada en la simulación de escenarios ideales así como realistas que demuestra que los filtros KF superan al resto. Esta misma parte de la tesis presenta el diseño genérico de estimadores "quadratic bounded" para sistemas dinámicos lineales cuya incertidumbre se encuentra acotada dentro de elipsoides. Estos estimadores maximizan un compromiso, ajustable por el usuario que contempla la velocidad de convergencia así como el volumen de la solución final que contiene el error de estimación. Se deriva un observador de perturbaciones para plataformas aéreas basado en el modelo de alto orden arriba mencionado. Finalmente, se presenta un análisis del desempeño de estimación en función de los parámetros de diseño del filtro.Postprint (published version

UAV perception for safe flight under physical interaction

The control of autonomous flying vehicles with navigation purposes is a challenging task. Complexity arises mainly due to the non-linearity and uncertainty inherently present in the flight mechanics and aircraft-air interactions. Recently, interest has grown for equipping unmanned vehicles with the capacity to interact with their environment, other vehicles or humans. This will enable interesting applications such as autonomous load carrying, aerial refueling or parcel delivering. Having measured the interaction wrenches ease the control problem which can be configured to reject disturbances or to take profit of them to fulfill mission objectives. This thesis will contribute to this area by providing perception solutions which use limited and low cost sensors that enable state and disturbance estimation for possible, but not restricted to, interaction scenarios. This thesis contain three parts. The first part, introduces basic concepts related to the navigation state, aircraft dynamics, and sensor models. In addition, the platform under study is presented and mathematical models associated to it are calibrated. The second part is devoted to the observability analysis and the design of state observers. Linear and non-linear observability analysis techniques are used to unveil that the state of quadrotors equipped with GPS, magnetometers an IMU sensors cannot be uniquely identified in some specific flight configurations. Results of this section are relevant because the conflicting flight configurations contain hover, a flight maneuverer central in many unmanned aerial missions of VtoL vehicles. For many possible singular configurations, insightful descriptions and interpretations of the solution space known as indistinguishable region is provided. Findings are verified in simulation scenarios where it can be seen how a filter fails to recover the true state of an aircraft when imposing the hover flight condition. We discuss then the design of Extended Kalman Filters for state estimation that considers the available sensors. Issues that are typically not reported in the literature, such as when to update or propagate in the estimator algorithm or which coordinate frame should be used to represent each state variable are discussed. This leads to the formulation of four potentially equivalent but different discrete event-based filters for which precise algorithmic expressions are given. We compare the results of the four filters in simulation under known favorable conditions for observability. In order to diminish the effect of flying in the conflicting observability configurations, we provide an alternative filter based on the Schmidt Kalman Filter (SKF). The proposed filter shares the structure of the EKF, behaves better in the instants that the EKF fails and provides similar results in the remaining conditions. The last part of the thesis deals with the estimation of external disturbances. Disturbance estimation results are based on the derivation of a linear model for the aircraft dynamics which then extended with a high order disturbance model to enable the estimation of fast varying disturbances. Two external disturbance estimators from the literature are reviewed and adapted to the new model. Also, two Kalman observers that exploit the linearity of the derived model are presented. A simulation comparison is provided demonstrating that the KF disturbance estimators outperform the other. In addition, this part presents a design methodology of generic quadratic bounded observers for linear systems with ellipsoidal bounded uncertainty. The derived observers maximize a user tunable compromise between the estimation convergence speed and the final volume containing the estimation error. An observer for disturbances acting on a flying platform is derived considering the high order disturbance model above mentioned. Finally, an analysis of the estimation performance with respect to the design parameters is presented.Esta tesis, contribuye en este área formulando soluciones de percepción que permiten la estimación del estado y perturbaciones externas en condiciones normales de vuelos así como casos de interacción para UAVs equipados con sensores limitados y de bajo coste. La tesis se estructura en tres partes. La primera de ellas introduce los conceptos básicos relacionados con el estado de navegación, la dinámica de la aeronave y modelos de sensores. Además, se presenta la plataforma de estudio así como los modelos matemáticos asociados a ella y su calibración. La segunda parte está destinada al análisis de observabilidad y el diseño de observadores de estado. Los resultados de esta sección son importantes porque dentro de las condiciones de vuelo conflictivas se encuentra el vuelo a punto fijo, una maniobra de vuelo central en muchas misiones de vehículos VToL. Se analizan estas condiciones críticas de vuelo y para ellas se deriva y describe el espacio de soluciones posible conocido como región indistinguible. Los resultados son verificados en simulación dónde se puede apreciar como un estimador de estado falla al intentar realizar su tarea cuando la aeronave está en vuelo a punto fijo. Seguidamente se presenta el diseño de filtros extendidos de Kalman (EKF) que proveen estimaciones del estado con la información limitada de los sensores disponibles. Se discuten conceptos que habitualmente no se presentan en la literatura como cuando actualizar o propagar en el algoritmo de estimación o que sistema de referencia se debe utilizar para representar adecuadamente las variables de estado. Esto lleva a la formulación algorítmica de cuatro filtros discretos basados en eventos, diferentes, pero en esencia equivalentes. Se derivan rutinas de inicialización para los filtros y se comparan los resultados en simulación bajo condiciones favorables de estimación. Con la idea de disminuir el efecto de volar en configuraciones de observabilidad conflictivas, se deriva un filtro alternativo basado en el filtro de Schmidt Kalman (SKF). El filtro propuesto comparte estructura con el EKF, tiene un mejor comportamiento allí dónde le EKF falla y una respuesta similar en el resto de condiciones de vuelo. La última parte de la tesis trata con la estimación de perturbaciones externas. Para ello se deriva un modelo lineal que relaciona fuerzas y momentos con velocidades junto a un modelo de alto orden para las perturbaciones. Se estudia su aplicación a dos modelos para la estimación de perturbaciones ya presentes en la literatura. Además, se proponen dos nuevos filtros de Kalman que se aprovechan de la linealidad del modelo. Se presenta una comparativa basada en la simulación de escenarios ideales así como realistas que demuestra que los filtros KF superan al resto. Esta misma parte de la tesis presenta el diseño genérico de estimadores "quadratic bounded" para sistemas dinámicos lineales cuya incertidumbre se encuentra acotada dentro de elipsoides. Estos estimadores maximizan un compromiso, ajustable por el usuario que contempla la velocidad de convergencia así como el volumen de la solución final que contiene el error de estimación. Se deriva un observador de perturbaciones para plataformas aéreas basado en el modelo de alto orden arriba mencionado. Finalmente, se presenta un análisis del desempeño de estimación en función de los parámetros de diseño del filtro