A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks

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

Autonomous landing of fixed-wing aircraft on mobile platforms

E n esta tesis se propone un nuevo sistema que permite la operación de aeronaves autónomas sin tren de aterrizaje. El trabajo está motivado por el interés industrial en aeronaves con la capacidad de volar a gran altitud, con más capacidad de carga útil y capaces de aterrizar con viento cruzado. El enfoque seguido en este trabajo consiste en eliminar el sistema de aterrizaje de una aeronave de ala fija empleando una plataforma móvil de aterrizaje en tierra. La aeronave y la plataforma deben sincronizar su movimiento antes del aterrizaje, lo que se logra mediante la estimación del estado relativo entre ambas y el control cooperativo del movimiento. El objetivo principal de esta Tesis es el desarrollo de una solución práctica para el aterrizaje autónomo de una aeronave de ala fija en una plataforma móvil. En la tesis se combinan nuevos métodos con experimentos prácticos para los cuales se ha desarrollado un sistema de pruebas específico. Se desarrollan dos variantes diferentes del sistema de aterrizaje. El primero presta atención especial a la seguridad, es robusto ante retrasos en la comunicación entre vehículos y cumple procedimientos habituales de aterrizaje, al tiempo que reduce la complejidad del sistema. En el segundo se utilizan trayectorias optimizadas del vehículo y sincronización bilateral de posición para maximizar el rendimiento del aterrizaje en términos de requerimientos de longitud necesaria de pista, pero la estabilidad es dependiente del retraso de tiempo, con lo cual es necesario desarrollar un controlador estabilizador ampliado, basado en pasividad, que permite resolver este problema. Ambas estrategias imponen requisitos funcionales a los controladores de cada uno de los vehículos, lo que implica la capacidad de controlar el movimiento longitudinal sin afectar el control lateral o vertical, y viceversa. El control de vuelo basado en energía se utiliza para proporcionar dicha funcionalidad a la aeronave. Los sistemas de aterrizaje desarrollados se han analizado en simulación estableciéndose los límites de rendimiento mediante múltiples repeticiones aleatorias. Se llegó a la conclusión de que el controlador basado en seguridad proporciona un rendimiento de aterrizaje satisfactorio al tiempo que suministra una mayor seguridad operativa y un menor esfuerzo de implementación y certificación. El controlador basado en el rendimiento es prometedor para aplicaciones con una longitud de pista limitada. Se descubrió que los beneficios del controlador basado en el rendimiento son menos pronunciados para una dinámica de vehículos terrestres más lenta. Teniendo en cuenta la dinámica lenta de la configuración del demostrador, se eligió el enfoque basado en la seguridad para los primeros experimentos de aterrizaje. El sistema de aterrizaje se validó en diversas pruebas de aterrizaje exitosas, que, a juicio del autor, son las primeras en el mundo realizadas con aeronaves reales. En última instancia, el concepto propuesto ofrece importantes beneficios y constituye una estrategia prometedora para futuras soluciones de aterrizaje de aeronaves.In this thesis a new landing system is proposed, which allows for the operation of autonomous aircraft without landing gear. The work was motivated by the industrial need for more capable high altitude aircraft systems, which typically suffer from low payload capacity and high crosswind landing sensitivity. The approach followed in this work consists in removing the landing gear system from the aircraft and introducing a mobile ground-based landing platform. The vehicles must synchronize their motion prior to landing, which is achieved through relative state estimation and cooperative motion control. The development of a practical solution for the autonomous landing of an aircraft on a moving platform thus constitutes the main goal of this thesis. Therefore, theoretical investigations are combined with real experiments for which a special setup is developed and implemented. Two different landing system variants are developed — the safety-based landing system is robust to inter-vehicle communication delays and adheres to established landing procedures, while reducing system complexity. The performance-based landing system uses optimized vehicle trajectories and bilateral position synchronization to maximize landing performance in terms of used runway, but suffers from time delay-dependent stability. An extended passivity-based stabilizing controller was implemented to cope with this issue. Both strategies impose functional requirements on the individual vehicle controllers, which imply independent controllability of the translational degrees of freedom. Energy-based flight control is utilized to provide such functionality for the aircraft. The developed landing systems are analyzed in simulation and performance bounds are determined by means of repeated random sampling. The safety-based controller was found to provide satisfactory landing performance while providing higher operational safety, and lower implementation and certification effort. The performance-based controller is promising for applications with limited runway length. The performance benefits were found to be less pronounced for slower ground vehicle dynamics. Given the slow dynamics of the demonstrator setup, the safety-based approach was chosen for first landing experiments. The landing system was validated in a number of successful landing trials, which to the author’s best knowledge was the first time such technology was demonstrated on the given scale, worldwide. Ultimately, the proposed concept offers decisive benefits and constitutes a promising strategy for future aircraft landing solutions

Armstrong Research, Technology, and Engineering Accomplishments 2014

No abstract availabl

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Application of robust control in unmanned vehicle flight control system design

The robust loop-shaping control methodology is applied in the flight control system design of the Cranfield A3 Observer unmanned, unstable, catapult launched air vehicle. Detailed linear models for the full operational flight envelope of the air vehicle are developed. The nominal and worst-case models are determined using the v-gap metric. The effect of neglecting subsystems such as actuators and/or computation delays on modelling uncertainty is determined using the v-gap metric and shown to be significant. Detailed designs for the longitudinal, lateral, and the combined full dynamics TDF controllers were carried out. The Hanus command signal conditioning technique is also implemented to overcome actuator saturation and windup. The robust control system is then successfully evaluated in the high fidelity 6DOF non-linear simulation to assess its capability of launch stabilization in extreme cross-wind conditions, control effectiveness in climb, and navigation precision through the prescribed 3D flight path in level cruise. Robust performance and stability of the single-point non-scheduled control law is also demonstrated throughout the full operational flight envelope the air vehicle is capable of and for all flight phases and beyond, to severe launch conditions, such as 33knots crosswind and exaggerated CG shifts. The robust TDF control law is finally compared with the classical PMC law where the actual number of variables to be manipulated manually in the design process are shown to be much less, due to the scheduling process elimination, although the size of the final controller was much higher. The robust control law performance superiority is demonstrated in the non-linear simulation for the full flight envelope and in extreme flight conditions

A Nature inspired guidance system for unmanned autonomous vehicles employed in a search role.

Since the very earliest days of the human race, people have been studying animal behaviours. In those early times, being able to predict animal behaviour gave hunters the advantages required for success. Then, as societies began to develop this gave way, to an extent, to agriculture and early studies, much of it trial and error, enabled farmers to successfully breed and raise livestock to feed an ever growing population. Following the advent of scientific endeavour, more rigorous academic research has taken human understanding of the natural world to much greater depth. In recent years, some of this understanding has been applied to the field of computing, creating the more specialised field of natural computing. In this arena, a considerable amount of research has been undertaken to exploit the analogy between, say, searching a given problem space for an optimal solution and the natural process of foraging for food. Such analogies have led to useful solutions in areas such as numerical optimisation and communication network management, prominent examples being ant colony systems and particle swarm optimisation; however, these solutions often rely on well-defined fitness landscapes that may not always be available. One practical application of natural computing may be to create behaviours for the control of autonomous vehicles that would utilise the findings of ethological research, identifying the natural world behaviours that have evolved over millennia to surmount many of the problems that autonomous vehicles find difficult; for example, long range underwater navigation or obstacle avoidance in fast moving environments. This thesis provides an exploratory investigation into the use of natural search strategies for improving the performance of autonomous vehicles operating in a search role. It begins with a survey of related work, including recent developments in autonomous vehicles and a ground breaking study of behaviours observed within the natural world that highlights general cooperative group behaviours, search strategies and communication methods that might be useful within a wider computing context beyond optimisation, where the information may be sparse but new paradigms could be developed that capitalise on research into biological systems that have developed over millennia within the natural world. Following this, using a 2-dimensional model, novel research is reported that explores whether autonomous vehicle search can be enhanced by applying natural search behaviours for a variety of search targets. Having identified useful search behaviours for detecting targets, it then considers scenarios where detection is lost and whether natural strategies for re-detection can improve overall systemic performance in search applications. Analysis of empirical results indicate that search strategies exploiting behaviours found in nature can improve performance over random search and commonly applied systematic searches, such as grids and spirals, across a variety of relative target speeds, from static targets to twice the speed of the searching vehicles, and against various target movement types such as deterministic movement, random walks and other nature inspired movement. It was found that strategies were most successful under similar target-vehicle relationships as were identified in nature. Experiments with target occlusion also reveal that natural reacquisition strategies could improve the probability oftarget redetection

Aeronautical Engineering. A continuing bibliography, supplement 112

This bibliography lists 424 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1979

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin