Dynamic model and control of a new quadrotor unmanned aerial vehicle with tilt-wing mechanism

In this work a dynamic model of a new quadrotor aerial vehicle that is equipped with a tilt-wing mechanism is presented. The vehicle has the capabilities of vertical take-off/landing (VTOL) like a helicopter and flying horizontal like an airplane. Dynamic model of the vehicle is derived both for vertical and horizontal flight modes using Newton-Euler formulation. An LQR controller for the vertical flight mode has also been developed and its performance has been tested with several simulations

Modeling and position control of a new quad-rotor unmanned aerial vehicle with tilt-wing mechanism

In this work a dynamic model of a new quadrotor aerial vehicle that is equipped with a tilt-wing mechanism is presented. The vehicle has the capabilities of vertical take-off/landing (VTOL) like a helicopter and flying horizontal like an airplane. Dynamic model of the vehicle is derived both for vertical and horizontal flight modes using Newton-Euler formulation. An LQR controller for the vertical flight mode has also been developed and its performance has been tested with several simulations

Mathematical modeling and vertical flight control of a tilt-wing UAV

This paper presents a mathematical model and vertical flight control algorithms for a new tilt-wing unmanned aerial vehicle (UAV). The vehicle is capable of vertical take-off and landing (VTOL). Due to its tilt-wing structure, it can also fly horizontally. The mathematical model of the vehicle is obtained using Newton-Euler formulation. A gravity compensated PID controller is designed for altitude control, and three PID controllers are designed for attitude stabilization of the vehicle. Performances of these controllers are found to be quite satisfactory as demonstrated by indoor and outdoor flight experiments

Active Energy Management Based on Meta-Heuristic Algorithms of Fuel Cell/Battery/Supercapacitor Energy Storage System for Aircraft

This paper presents the application of an active energy management strategy to a hybrid system consisting of a proton exchange membrane fuel cell (PEMFC), battery, and supercapacitor. The purpose of energy management is to control the battery and supercapacitor states of charge (SOCs) as well as minimizing hydrogen consumption. Energy management should be applied to hybrid systems created in this way to increase efficiency and control working conditions. In this study, optimization of an existing model in the literature with different meta-heuristic methods was further examined and results similar to those in the literature were obtained. Ant lion optimizer (ALO), moth-flame optimization (MFO), dragonfly algorithm (DA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), particle swarm optimization (PSO), and whale optimization algorithm (WOA) meta-heuristic algorithms were applied to control the flow of power between sources. The optimization methods were compared in terms of hydrogen consumption and calculation time. Simulation studies were conducted in Matlab/Simulink R2020b (academic license). The contribution of the study is that the optimization methods of ant lion algorithm, moth-flame algorithm, and sine cosine algorithm were applied to this system for the first time. It was concluded that the most effective method in terms of hydrogen consumption and computational burden was the sine cosine algorithm. In addition, the sine cosine algorithm provided better results than similar meta-heuristic algorithms in the literature in terms of hydrogen consumption. At the same time, meta-heuristic optimization algorithms and equivalent consumption minimization strategy (ECMS) and classical proportional integral (PI) control strategy were compared as a benchmark study as done in the literature, and it was concluded that meta-heuristic algorithms were more effective in terms of hydrogen consumption and computational time

A knowledge extraction framework for crime analysis: unsupervised methods in uncertain environments

Cette thèse de doctorat investigue le rôle des méthodes d'extraction de connaissances dans l'analyse criminelle en tant que projet interdisciplinaire, avec une orientation sur les méthodes non supervisées traitant les aspects d'incertitude qui sont intrinsèques à l'environnement du crime. Dans un contexte où les données générées par les activités criminelles sont de plus en plus disponibles grâce à l'évolution des technologies, l'utilisation de méthodes automatisées pour créer de la valeur à partir de ces données devient une nécessité. Ces méthodes d'analyse requièrent une conception spécifique selon la nature des données qu'elles traitent, principalement collectées à partir de scènes de crimes. Les analystes criminels ont désespérément besoin de telles méthodes pour être mieux informés et efficients dans la lutte perpétuelle contre le crime. Cependant, leurs choix en termes d’étendue et de disponibilité sont très limités. Un framework qui délimite et explique le rôle des méthodes d’extraction de connaissance pour l’analyse criminelle est proposé. Ce framework adresse un défi particulier : développer des méthodes de data mining non supervisées qui permettent de traiter l’incertitude des données criminelles. Trois approches sont développées pour confronter ce défi. (1) Comment structurer et représenter des données criminelles pour exploiter pleinement leur potentiel à révéler des connaissances par la conduite d’autres analyses ? (2) Quelle est la méthode appropriée d’analyse de liens entre les crimes qui prenne en compte des données à la fois quantitatives et qualitatives ? Et (3) quelle est la méthode appropriée pour aider les analystes criminels à détecter des changements dans des tendances criminelles d’une manière flexible et compréhensible ? L’importance de cette recherche interdisciplinaire peut être résumée en deux points : elle clarifie et délimite le rôle du data mining dans l’analyse criminelle, en fournissant une perspective sur son applicabilité dans cet environnement particulier ; et elle facilite l’extraction de connaissances par l’utilisation des méthodes proposée guidées par le métier.This doctoral thesis investigates the role of knowledge extraction methods in the analysis of crime as an interdisciplinary project, with a focus on unsupervised methods dealing with the uncertain aspects that are intrinsic to the crime environment. In a context where data generated from criminal activities are increasingly available due to the evolution of technology, the use of automated methods to create value from these data becomes a necessity. These analytic methods require a specific design with regard to the nature of the data they deal with, mostly gathered from crime scenes. Crime analysts desperately need such methods to be better informed and efficient in the perpetual struggle against crime. However, their choices in terms of range and availability are very limited. A framework delineating and explaining the role of knowledge extraction methods for crime analysis is provided. This framework addresses a particular challenge: developing unsupervised data mining methods dealing with the uncertainty of crime data. Three approaches are developed to confront this challenge. (1) How to structure and represent crime data to fully exploit the potential of revealing knowledge with further analyses? (2) What is the appropriate method to analyze links between crimes that can deal with both qualitative and quantitative crime data? And (3) what is the appropriate method to help crime analysts to flexibly and understandably detect changes in crime trends? The significance of this interdisciplinary research can be summarized in two points: it clarifies and delineates the role of data mining in crime analysis, by giving some insights into its applicability in this particular environment; and it makes easier the extraction of knowledge by the use of the proposed domain-driven methods

Numerical analysis of flow around bluff bodies with 4th and 2nd order compact formulations

In the present study, fourth-order compact formulation has been improved for Navier-Stokes (N-S) equations, which is expressed for two-dimensional, steady, incompressible flow problems. N-S equation system has been expressed with Stream Function-Vorticity Approach using Finite Difference Method (FDM) from the numerical methods. In order to test the functionality and applicability of the improved numerical formulation, a sample for submerged bluff bodies, flow problem around cylinder with square cross-section was chosen as a benchmark problem. As a result of applying improved numerical formulation with Gauss-Seidel Relaxation Method was used for this benchmark problem. The benchmark problem was also solved with second-order accuracy and obtained numerical results were compared with fourth-order accuracy numerical results. With the same Reynolds Number and the same free-stream velocity values, fourth-order numerical results are more convergent than second-order numerical results. Furthermore, in the flow field for considered benchmark problem, separation bubble length that consisted in the wake region is increased proportionally depending on the alteration of the Reynolds Number values