Ingeniero Civil Eléctrico / En esta Memoria de Título se diseñó y se implementó un sistema de control asistido para una plataforma de vuelo no tripulada (Unmanned Aerial Vehicle - UAV). Lo anterior se realizó con el propósito de simplificar el proceso de aprendizaje de operación radio-controlada a través de un mando de 4 canales. La plataforma utilizada correspondió a un quad-rotor, nave consistente en un cuerpo y cuatro hélices propulsoras. La tele-operación de UAVs constituye una tarea de lento aprendizaje debido a diversos factores como la dinámica de vuelo y la familiaridad del operario con los mandos. El principal objetivo del trabajo es el diseño y la implementación de un sistema que evalúe el desempeño del operario a través de un análisis de sus instrucciones y de la evolución de la nave, y que actúe a nivel de los controladores, facilitando en alguna medida el manejo de la nave. Este trabajo se realizó con el apoyo del Grupo de Automatización del Centro Avanzado de Tecnología para la Minería (AMTC). La metodología de trabajo consistió, en primer lugar, en la integración de la plataforma aérea. Esta tarea incluyó el montaje de la estructura principal, la unión de todos los componentes mecánicos y electrónicos, el diagnóstico de los módulos utilizados, la instalación de un programa núcleo (firmware) de código abierto (APM: Copter), y la sintonización inicial de parámetros. Una vez lista la plataforma, se estudió el código fuente para determinar el tipo de asistente que se utilizaría. Posteriormente, se diseñó un sistema asistente de vuelo en base a pruebas de vuelo y un post-procesamiento de los datos realizado fuera de línea. Los sensores disponibles incluían las unidades de medición inercial, consistentes en acelerómetros y giróscopos, además de magnetómetros. Estos sensores no permitían una estimación adecuada de la posición de la nave, por lo que se consideró además la realización de simulaciones en base a los comandos entregados por el operario al momento dar la prueba. Se utilizó un simulador compatible con el código fuente utilizado y se escribió un programa en Python para enviar las órdenes al simulador mediante el protocolo de comunicación Mavlink y obtener coordenadas simuladas aproximadas. Posteriormente se creó un programa en MATLAB para analizar los resultados de ambas pruebas. Por otro lado, se modificó el código fuente, escrito en C++, creando un modo de vuelo que filtra las referencias entregadas a los controladores PID de alto nivel en función de dos parámetros que deben ser ajustados manualmente. Se realizaron pruebas con diferentes operarios para verificar tanto el funcionamiento del simulador como del asistente de vuelo. Las simulaciones realizadas entregaron resultados imprecisos, con valores llegando sobre los 100 [m] para pruebas que no tuvieron desplazamientos superiores a los 3 [m]. Se consideró apropiado descartar su uso para la obtención de parámetros. Con ellos, se realizaron pruebas de operación para mostrar el efecto del asistente. Los operarios mostraron un grado de mejoría en las pruebas, específicamente pudiendo controlar la altitud de la plataforma con más estabilidad. Se obtuvieron oscilaciones lentas en los ángulos, sin embargo se mantuvieron dentro de rangos estables. Se concluyó que el asistente logra efectivamente intervenir de manera positiva en la estabilización de la nave, ayudando al control de los ángulos horizontales. Esto permite al usuario en primer lugar aprender a controlar la aceleración de la nave. Se proponen mejoras al trabajo, incluyendo la posible utilización de sensores de mayor precisión como GPS para la posición o láser o sonar para la altitud, lo que permitiría la posibilidad de asistir el control de aceleración y de obtener lecturas menos erráticas.

This dissertation covers the process of modelling and subsequently developing a flight dynamics controller for a quad–rotor unmanned aerial vehicle. It is a theoretical study that focusses on the selection of a controller type by first analysing the problem on a system level and then on a technical level. The craft is modelled using the Newton– Euler model, accounting for multiple reference frames to account for the interpretation of orientation as seen by on–board sensors. The quad–rotor model and selected controllers are characterized and compared. The model is verified through simulation by comparison to a validated model. A series of generic control loops are derived and used as reference for the implementation of the controllers. A Simulator is developed and used to do a comparative study of the various controller types and the control approach. Finally a full simulation is done to demonstrate the interaction between the controllers. / Thesis (MIng (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2012.

This dissertation covers the process of modelling and subsequently developing a flight dynamics controller for a quad–rotor unmanned aerial vehicle. It is a theoretical study that focusses on the selection of a controller type by first analysing the problem on a system level and then on a technical level. The craft is modelled using the Newton– Euler model, accounting for multiple reference frames to account for the interpretation of orientation as seen by on–board sensors. The quad–rotor model and selected controllers are characterized and compared. The model is verified through simulation by comparison to a validated model. A series of generic control loops are derived and used as reference for the implementation of the controllers. A Simulator is developed and used to do a comparative study of the various controller types and the control approach. Finally a full simulation is done to demonstrate the interaction between the controllers. / Thesis (MIng (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2012.

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.

A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.