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This paper proposes a fuzzy logic based autonomous navigation controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed under the main navigation system for the control of the altitude, the speed, and the heading, through which the global position (latitude–longitude) of the air vehicle is controlled. A SID (Standard I...
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... shown in Fig. 4, there are two computers in an autonomous UAV. One of them is the flight computer and the other is the mission (navigation) computer. UAV flight computer basically sets the control surfaces to the desired positions by managing the servo controllers in the defined flight envelope supplied by the UAV navigation computer as a command, ...
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An optimized fuzzy logic has been considered to control a VTOL ducted fan UAV during the vertical flight. The control design goal is to have stable vertical flight in the presence of wind disturbance. Genetic Algorithm has been used as the tool for optimization. The controller inputs are body components velocity and roll rate error, whereas the out...
Citations
... The Fuzzy Logic Toolbox provides MATLAB functions and a Simulink block for analyzing, designing, and simulating systems based on fuzzy logic, as described in Figure 4. The authors in [58][59][60][61][62] have also used the Fuzzy Logic in the modeling of their proposed work. ...
According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.
... Several centralized and decentralized architectures for UAVs have been proposed over the years [48,49]. Optimization of trajectories and autonomous navigation has been explored in detail using different UAV transportation modalities [50], including ground [51], aerial [52,53] and underwater [54] modalities. In terms of cooperation between UAVs, models have been designed to achieve common goals through cooperation [55,56], this includes swarm [57], clustering [58] and social [59] cooperation models. ...
Pervasive applications are revolutionizing the perception that users have towards the environment. Indeed, pervasive applications perform resource-intensive computations over large amounts of stream sensor data collected from multiple sources. This allows applications to provide richer and deep insights into the natural characteristics that govern everything that surrounds us. A key limitation of these applications is that they have high energy footprints, which in turn hampers the quality of experience of users. While cloud and edge computing solutions can be applied to alleviate the problem, these solutions are hard to adopt in existing architecture and far from become ubiquitous. Fortunately, cloudlets are becoming portable enough, such that they can be transported and integrated into any environment easily and dynamically. In this article, we investigate how cloudlets can be transported by unmanned autonomous vehicles (UAV)s to provide computation support on the edge. Based on our study, we develop GEESE, a novel UAV-based system that enables the dynamic deployment of an edge computing infrastructure through the cooperation of multiple UAVs carrying cloudlets. By using GEESE, we conduct rigorous experiments to analyze the effort to deliver cloudlets using aerial, ground, and underwater UAVs. Our results indicate that UAVs can work in a cooperative manner to enable edge computing in the wild.
... To make use of them with the most advantageous way, controlling systems for UAVs are vital. There are several approaches in the literature for the controlling such as adaptive systems [13], artificial neural networks [14], evolutionary algorithms [15], fuzzy logic and fuzzy inference systems [12,16]. ...
The utilizations of Unmanned Aerial Vehicles (UAVs) have become increasingly more popular today and been successfully demonstrated for various civil and military applications. In these applications, one of the most critical issue is networking between UAVs and ground control points due to requirements of efficient control and sustainable path for data transmission. Since UAV nodes are extremely dynamic and provide a vast range of applications, selection process of the most appropriate network controller technology is extremely complex and can be considered as a multi-criteria decision making (MCDM) problem. In this paper, a neutrosophic TOPSIS method has been suggested to evaluate the network controllers and relays of the UAV alternatives. The proposed methodology is applied for the evaluation of the most appropriate control technology for UAVs.
... Fuzzy logic is a model-free, intuitive design that can be built up and trained for specific applications. The fuzzy logic UAV controller follows a (if event A, then event B) framework based on the rule base, meaning that it indirectly deals with aerodynamic uncertainties in the UAV model [87,88]. In the case of new generation aircraft however, where aerodynamic uncertainties have time-varying structure, several simulation or flight tests will be needed to train the system and the designed controller to achieve robust performance for every on-demand change of the aerodynamic coefficients (event A). ...
This paper presents a survey of controller design techniques aimed at autonomous navigation and control of Unmanned Aerial Vehicles (UAVs), focusing on the challenge of aerodynamic uncertainty. Although many roadblocks exist, the most significant and challenging task for UAV navigation and control is the one of aerodynamic/model uncertainty. Current autopilots and controller designs for autonomous airplanes are mainly concerned with the feature of constant, unknown aerodynamic parameters, i.e., control and stability derivatives of the platform. This research focuses on a thorough investigation of the related theory and its applicability, centering on specific techniques that are able to control UAVs with rapidly changing, time-varying aerodynamic characteristics during flight. The scientific merit of this work is the comprehensive overview provided and the technical study that is performed, highlighting the advantages and disadvantages for each technique with respect to its efficiency and performance.
... Several of these methods require a more or less accurate aerodynamic model of the UAV. A model-free method based on fuzzy logic can be found in [15]. Fuzzy control falls under the category of intelligent control systems, which also includes the use of neural networks. ...
... Each UAV in the team takes its own decision in a decentralized manner, to reconfigure the formation of the whole team ensuring the execution of the required mission in case of fault occurrence. A set of three FLCs algorithm were introduced in [20] to control the navigation of a fleet of UAVs. The set of FLCs control the altitude, speed and the heading, respectively. ...
This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.
... Where the weight of each factor of maneuvering performance was calculated by a multi-objective optimization algorithm in the search space. Kurnaz, et al. [12] used tactical air navigation (TACAN) approach and the performance of the fuzzy based controllers is evaluated with time-based diagrams, the simulation studies presented verify that the UAV can follow the predefined trajectories despite the simplicity of the controllers. Kurnaz, et al. [13] evaluated the performance of adaptive neuro-fuzzy inference system (ANFIS) based controllers in relation to the autonomous operation of UAVs, nevertheless, for some flight conditions, the ANFIS type controller has resulted in unstable performance. ...
... The paper [6] by describes the model of an autopilot controller that is based on fuzzy algorithms. The paper by [5] proposes a fuzzy logic based autonomous navigation controller for UAVs (unmanned aerial vehicles). In an article by [4], a new fuzzy logic method is proposed to add vessel collision avoidance capability to VTS/AIS systems for all potential collision ships in the surveillance area. ...
... Flight tests needed to be trained, stability not guaranteed, many tuning parameters [9], [23], [35], [36] H∞ Control Can handle parameter uncertainty Complexity issues, optimal with respect to predefined cost function [5], [15], [16], [17] μ-synthesis Considers specifications, parameter uncertainty, disturbances, sensor and actuator noise NP-complete [37], [38], [39], [40] surface but instead, it may oscillate around the surface due to delays in control switching in what is called chattering. Sliding mode is generating discontinuous control laws, raising questions about the existence and uniqueness of solutions and the validity of Lyapunov analysis [25]. ...
... In [36], PD, PI and PID fuzzy control techniques are implemented for flight control and navigation (altitude, speed, heading and inertial position) of the Aerosonde UAV. Results are simulation based using MatLab and Aerosim Aeronautical Simulation. ...
... This means that the propellers operate at full capacity and the back propeller can still turn the UAV. If we maintain the orientation even in a rough, windy environment, it is efficient and easier to control: turning the back propeller into the wind counteracts its force, and the switching blade UAV could easily be operated in these types of environments, whereas a quad-rotor UAV of the same size would not fly well in the abovementioned case [13][14][15]. ...
In this paper, a novel Model Reference Adaptive Control (MRAC)-based hybrid control algorithm is presented for the trajectory tracking of a tri-rotor Unmanned Aerial Vehicle (UAV). The mathematical model of the tri-rotor is based on the Newton–Euler formula, whereas the MRAC-based hybrid controller consists of Fuzzy Proportional Integral Derivative (F-PID) and Fuzzy Proportional Derivative (F-PD) controllers. MRAC is used as the main controller for the dynamics, while the parameters of the adaptive controller are fine-tuned by the F-PD controller for the altitude control subsystem and the F-PID controller for the attitude control subsystem of the UAV. The stability of the system is ensured and proven by Lyapunov stability analysis. The proposed control algorithm is tested and verified using computer simulations for the trajectory tracking of the desired path as an input. The effectiveness of our proposed algorithm is compared with F-PID and the Fuzzy Logic Controller (FLC). Our proposed controller exhibits much less steady state error, quick error convergence in the presence of disturbance or noise, and model uncertainties.
