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

Multi-authored monograph

Unmanned aerial vehicles. Perspectives. Management. Power supply : Multi-authored monograph / V. V. Holovenskiy, T. F. Shmelova,Y. M. Shmelev and oth.; Science Editor DSc. (Engineering), T. F. Shmelova. – Warsaw, 2019. – 100 p. - ISBN 978-83-66216-10-5.У монографії аналізуються можливі варіанти енергопостачання та управління безпілотними літальними апаратами. Також розглядається питання прийняття рішення оператором безпілотного літального апарату при управлінні у надзвичайних ситуаціях. Рекомендується для фахівців, аспірантів і студентів за спеціальностями 141 - «Електроенергетика, електротехніка та електромеханіка», 173 - «Авіоніка» та інших суміжних спеціальностей.The monograph analyzes the possible options for energy supply and control of unmanned aerial vehicles. Also, the issue of decision-making by the operator of an unmanned aerial vehicle in the management of emergencies is considered.

Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

We describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Autonomous Approach and Landing Algorithms for Unmanned Aerial Vehicles

In recent years, several research activities have been developed in order to increase the autonomy features in Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. The worldwide research community agrees with the opportunity of the dual use of UAVs (for both military and civil purposes), for this reason it is very important to make the UAVs and their autolanding systems compliant with the actual and future rules and with the procedures regarding autonomous flight in ATM (Air Traffic Management) airspace in addition to the typical military aims of minimizing fuel, space or other important parameters during each autonomous task. Developing autolanding systems with a desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guide, navigation and control disciplines. The main drawbacks of the autolanding systems available at the state of art concern or the lack of adaptivity of the trajectory generation and tracking to unpredicted external events, such as varied environmental condition and unexpected threats to avoid, or the missed compliance with the guide lines imposed by certification authorities of the proposed technologies used to get the desired above mentioned adaptivity. During his PhD period the author contributed to the development of an autonomous approach and landing system considering all the indispensable functionalities like: mission automation logic, runway data managing, sensor fusion for optimal estimation of vehicle state, trajectory generation and tracking considering optimality criteria, health management algorithms. In particular the system addressed in this thesis is capable to perform a fully adaptive autonomous landing starting from any point of the three dimensional space. The main novel feature of this algorithm is that it generates on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line trajectory re-planning in the landing phase, fully autonomy from remote pilot inputs, weakly instrumented landing runway (without ILS availability), ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions, decision-making logic evaluation for key-decisions regarding possible execution of altitude recovery manoeuvre based on the Differential GPS integrity signal and compatible with the functionalities made available by the future GNSS system. All the algorithms developed allow reducing computational tractability of trajectory generation and tracking problems so as to be suitable for real time implementation and to still obtain a feasible (for the vehicle) robust and adaptive trajectory for the UAV. All the activities related to the current study have been conducted at CIRA (Italian Aerospace Research Center) in the framework of the aeronautical TECVOL project whose aim is to develop innovative technologies for the autonomous flight. The autolanding system was developed by the TECVOL team and the author’s contribution to it will be outlined in the thesis. Effectiveness of proposed algorithms has been then evaluated in real flight experiments, using the aeronautical flying demonstrator available at CIRA

Autonomous Landing and Go-around of Large Jets Under Severe Weather Conditions Using Artificial Neural Networks

We introduce the Intelligent Autopilot System (IAS) which is capable of autonomous landing, and go-around of large jets such as airliners under severe weather conditions. The IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to autonomously handle flight uncertainties such as severe weather conditions, autonomous complete flights, and go-around. A robust approach to control the aircraft's bearing using Artificial Neural Networks is proposed. An Artificial Neural Network predicts the appropriate bearing to be followed given the drift from the path line to be intercepted. In addition, the capabilities of the Flight Manager of the IAS are extended to detect unsafe landing attempts, and generate a go-around flight course. Experiments show that the IAS can handle such flight skills and tasks effectively, and can even land aircraft under severe weather conditions that are beyond the maximum demonstrated landing of the aircraft model used in this work as reported by the manufacturer's operations limitations. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots

Adaptive nonlinear guidance law using neural networks applied to a quadrotor

© 2019IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The NonLinear Guidance Law (NLGL) is a geometric algorithm commonly employed to solve the path following problem on different unmanned vehicles. NLGL is simple (does no depend on the model of the vehicle), effective and has only one tunning parameter. Its control parameter (L) depends on various factors, such as the velocity of the vehicle, the shape of the reference path and the dynamics of the vehicle. This paper analyses the effect of parameter L on the performance of NLGL when it is applied to a quadrotor vehicle. An Adaptive NLGL, which includes a velocity reduction term, is proposed. Stability proofs are given. Simulation results show that the proposed algorithm enhances the performance of the standard NLGL. Furthermore, it has no parameters to tune.Peer ReviewedPostprint (author's final draft

Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression and Artificial Neural Networks

A dataset of 854 small unmanned aerial system (SUAS) flight experiments from 2005-2009 is analyzed to determine significant factors that contribute to mishaps. The data from 29 airframes of different designs and technology readiness levels were aggregated. 20 measured parameters from each flight experiment are investigated, including wind speed, pilot experience, number of prior flights, pilot currency, etc. Outcomes of failures (loss of flight data) and damage (injury to airframe) are classified by logistic regression modeling and artificial neural network analysis. From the analysis, it can be concluded that SUAS damage is a random event that cannot be predicted with greater accuracy than guessing. Failures can be predicted with greater accuracy (38.5% occurrence, model hit rate 69.6%). Five significant factors were identified by both the neural networks and logistic regression. SUAS prototypes risk failures at six times the odds of their commercially manufactured counterparts. Likewise, manually controlled SUAS have twice the odds of experiencing a failure as those autonomously controlled. Wind speeds, pilot experience, and pilot currency were not found to be statistically significant to flight outcomes. The implications of these results for decision makers, range safety officers and test engineers are discussed

Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles

Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio

Adaptive Flight Control Research at NASA

A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing