Automated System of Stabilization and Position Control of Aviation Equipment

In this chapter, the author presents the problems of design of the robust automated system for stabilization and control of platforms with aircraft observation equipment. The mathematical model of the triaxial stabilized platform is developed. The procedure of synthesis of robust stabilization system based on robust structural synthesis is represented. The above-mentioned procedure uses loop-shaping approach and method of the mixed sensitivity. The matrix weighting transfer functions are obtained. The optimization programs in MatLab are developed. The developed procedures are approved based on the results of simulation by means of the appropriate Simulink model. The obtained results can be useful for unmanned aerial vehicles and aircraft of special aviation, which are used for monitoring technical objects and aerial photography. The technical contributions are procedures of the robust controller design represented as the flowchart. The proposed approach is validated by application of the theoretical suppositions to the concrete example and appropriate simulation results.

Unmanned Aerial Vehicle Applications for Military GIS Task Solutions

In the chapter, the authors present the results of unmanned aerial vehicle (UAV) applications for military geoinformation system (GIS) task solutions. The results of the visual modelling of the revealing process of invisible military objects from one of point of observation on the mountainous terrain of the Azerbaijan Republic by using UAV are presented. The observation conditions between two points of the selected mountain terrain during battle operation have been investigated using GIS technology. The quantitative method of the invisible area assessment and military objects in mountainous terrain are developed and offered by using UAVs. The numerical estimation method of a task support success of UAV reconnaissance flight in mountainous battle conditions has been offered and considered. Using UAVs for the purpose of orthophotomap making of the terrain and combat control the detailed 3D-model has been constructed.

Forest Firefighting Monitoring System Based on UAV Team and Remote Sensing

In this chapter, the authors propose an approach to using a heterogeneous team of unmanned aerial vehicles and remote sensing techniques to perform tactical forest firefighting operations. The authors present the three-level architecture of the multi-UAV-based forest firefighting monitoring system; features of patrolling, confirming, and monitoring missions; as well as functions of UAV in such missions. The authors consider an infrastructure for the UAV ground support and equipment used for the UAVs control. The method of the data integration into a fire-spreading model in a real-time DSS for the forest fire response is proposed. The proposed approach has been tested with the multi-UAV team that included three drones for the patrol missions, one helicopter for the confirmation mission, and one octocopter for the monitoring mission. The performance of such multi-UAV team has been studied in the laboratory conditions. The result of the experiment has shown that the proposed approach provides required credibility and efficiency of fire prediction and response.

Preliminary Sizing and Performance Calculations of Unmanned Air Vehicles

In this chapter, preliminary sizing and performance calculations of unmanned air vehicles (UAV), with relation to its propulsion system, are explained. Starting with the mission profile of the air vehicle, which is one of the important design drivers, the UAV is sized for the requirements. The requirements in design consist of a set of target values (e.g., payload amount to be carried, range, endurance time, cost, etc.). Additionally, the parameters within mission profile such as cruising altitude and speed of aircraft affect engine type, power level required, fuel quantity, and therefore general dimensions and the gross weight of the aircraft. It is often an iterative process to size the air vehicle and engine together. UAV is designed in a loop of calculations in which sizing, flight performance, and engine performance are done for each phase of the mission or flight profile to satisfy the overall design mission and requirements.

Development Specifics of the Tower Controller Intelligent Training System

The activity of the tower controller is connected with the implementation of a rather complex set of measures to ensure takeoff and landing operations. In connection with this, the aviation specialist should possess the number of professional competencies, which are formed by mastering the theoretical material and consolidating it in practice. Frequently, the initial consolidation in practice is performed by using automated training systems, especially intelligent training systems are in a great demand. Therefore, this chapter is devoted to the specifics of the development of such type aviation-focused system, which are given on the example of the intelligent training system “Tower Controller.” The prototype of this system was developed at Flight Academy of the National Aviation University, Ukraine. Its key differential peculiarity is focused on major stages of the decision-making process of the tower controller instead of visualization from the tower window.

Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers

This chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype of the neural network for evaluating the timeliness and correctness of the decision making by ATCs has been developed. The new theoretical and practical tasks for simulation and pre-simulation training have been obtained using expert judgment method. The methodology for sentiment analyzing the airline customers' opinions has been proposed. In addition, the examples of artificial intelligence systems and expert systems by the authors, students and colleagues from National Aviation University, Ukraine and Gdansk University of Technology, Poland have been proposed.

Use of Big Data in Aviation

The expansion of data and its efficient handling is becoming a more popular tendency in recent times bringing new difficulties to learn new avenues. Data analytics can be done more proficiently with the availability of distributed architecture of not only SQL (NoSQL) databases. Technological advancements around us are changing very rapidly, and major shift is being carried out, a switch from relational to non-relational world. When moving from relational to non-relational models, database administrators face common problems due to the fact that NoSQL is a no-schema database. The purpose of conducting this research is to propose a mechanism by which the schema of a relational database management system and its data can be transformed into big data by following some standardize guidelines. This model can be quite useful for relational database administrators by enabling them to give attention to logical modeling rather than procedural writing for each and every SQL to NoSQL transition.

Model of Calculating the Weight and Centering of the Airship With Measurement Errors

In this chapter, the authors present an imitation model for calculating the weight and centering of the airship, taking into account the errors that arise in the process of determining these parameters. Also in this chapter there is the analysis of existing means, methods, and methods for determining the weight and alignment of the airship in domestic airlines and air companies. The authors made the research of the sources of errors and identified their types. This chapter provides a new model of the takeoff weight and balance of the aircraft determining process, as well as the weight distribution of passengers law. These materials allow simulating one of the main operations in the preparation for departure process.

Information Technologies for Learning Principles of Fault-Tolerant Systems

In this chapter, the authors present a methodology for developing a model-tracing cognitive tutor. The methodology is based on Bayesian probabilistic networks for generating pedagogical interventions. The presented probabilistic model increases fidelity assessment due to its ability of independently diagnosing the degree of mastery for every knowledge component involved in students' actions; fidelity assessment in education is the ability to represent students' cognitive states as close as possible for analysis and evaluation. The cognitive tutor was developed to promote a self-regulated learning approach with an open learner model. The open learner model let students change the learning flow by changing the assigned tasks. The authors explain in detail the structural construction and employed algorithms for developing a model-tracing cognitive tutor in the domain of fault-tolerant systems. Preliminary results and future work are also discussed to assess effectiveness of the proposed approach and its implication in actual educational programs.

Synthesis of the Laws of Motion Control of a UAV Group With Natural Obstacles

A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.