Model Of Unmanned Aerial Vehicle Research Articles

Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV

Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors’ previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller.

NMPC-based UAV-USV cooperative tracking and landing

The study aims to solve the problem of real time tracking and precise landing of unmanned aerial vehicle (UAV) during unmanned surface vehicle (USV) navigation. In this paper, a UAV-USV cooperative tracking and landing control strategy based on nonlinear model predictive control (NMPC) is proposed. Firstly, the UAV-USV heterogeneous intelligent body collaborative system is constructed based on the mathematical model of UAV and USV; secondly, the tracking controller is designed based on NMPC algorithm to ensure that the UAV can track the USV in real time; finally, a UAV-USV cooperative landing control strategy is proposed to realize the heave motion of the USV to the peak vertex, thus, the UAV completes the precise landing with the minimum impact. As the simulation experimental results show, the UAV-USV cooperative tracking and landing control scheme proposed in this paper can provide effective solution against real time tracking and accurate landing of UAV during the navigation of USV.

Channel Modeling and Characteristics Analysis under Different 3D Dynamic Trajectories for UAV-Assisted Emergency Communications.

This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics-including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)-were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications.

A Study of Collaborative Trajectory Planning Method Based on Starling Swarm Bionic Algorithm for Multi-Unmanned Aerial Vehicle

This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV’s physical model properties.

Computational simulation study on disturbance of six-rotor UAVs due to ammunition launch

According to the multi-body dynamics theory, we analyzed and derived the influence laws of the ammunition launch angle, launch timing, mounting position, and mass on the stability of unmanned aerial vehicles (UAVs) by establishing a flight dynamics model of a six-rotor UAV in the process of ammunition launch. The analyses indicated that the ammunition launch angle, the longitudinal mounting position, and the multi-round ammunition launch timing significantly affect the disturbance of the UAV. When single-round ammunition is launched, selecting the appropriate ammunition mounting position can reduce the disturbance amplitude of the position of the UAV, and when multi-round ammunition is launched, the launch timing significantly affects the overall stability of the UAV. On the basis of the overall system and ammunition-related parameters, the optimal launch timing for different deployment methods can be derived. Through modeling analysis, we can qualitatively study the stability of the system during ammunition launch, providing a reference for the design of the ammunition launcher structure and the strike timing.

Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.

Concept of a Modular Multirotor Heavy Lift Unmanned Aerial Vehicle Platform

This paper presents a novel concept of a modular multirotor aerial robotic platform that can be used for specific profiles of heavy payload missions. A comprehensive mathematical model of the multirotor unmanned aerial vehicle (UAV) is presented, which is divided into the dynamic model and the control allocation scheme that describes the configuration of the aircraft. The components of the propulsion and energy module are selected, and the characterization of the propulsion units is carried out, as well as a preliminary analysis of the module parameters with regard to the considered payloads from 5 to 50 kg. The main goal is to design a modular aircraft that consists of easy-to-assemble modules and that enables the assembly of different propulsion and energy module configurations. Based on the analysis of system parameters, a modular aerial system is designed and the process of manufacturing using rapid prototyping technologies is presented. Based on the parameters obtained from the aircraft assembly CAD model and the implemented mathematical model, simulations are conducted for the two considered missions in the field of smart agriculture. For the purpose of conducting preliminary experiments, a quadrotor aircraft is built and tested in the case of attitude and remote control.

Edge Computing Enabled Energy-Efficient Multi-UAV Cooperative Target Search

Multiple unmanned aerial vehicles (UAVs) cooperative search have been widely adopted for surveillance and search-related applications. For a certain search area, UAVs may need to search it repeatedly to obtain a high-confidence result about the target distribution in the search area. However, the short battery life and moderate computational capability restrict UAVs to repeatedly execute the computation-intensive and energy-consuming search tasks. To address the issue, in this paper, we utilize edge computing to develop a continual and cooperative UAV search mechanism. Specifically, we first establish an edge computing enabled multi-UAV cooperative search framework, in which the mobility model of UAV, search task computing and offloading models are presented. An uncertainty minimization problem is then formulated, aiming to obtain a high-efficiency and high-confidence search result at the unpredictable uncertainty in search area. Considering that round-trip energy consumption, offloading decision-making, and trajectory planning may contribute to the reduction in uncertainty, we propose an uncertainty minimization-based cooperative target search (UMCTS) strategy. Finally, extensive simulation results validate that UMCTS can outperform the existing strategies and achieve at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$89\%$</tex-math></inline-formula> performance gain on average uncertainty. Based on the results, we also present a comprehensive analysis and discussion on how different parameters affect the search performance.

Development of model predictive controller in avoidance design of Unmanned Aerial Vehicle (UAV)

This research was accomplished by employing already developed mathematical model of a 6DoF UAV in free space through the motion of the 6DoF aircraft (unmanned aerial vehicle) determined by coordinate systems which allow an aircraft’s position and orientation in space to be kept tracked. The discrete method of the model developed for simulation in Simulink was realized with a suitable transfer function. Then, an artificial neural network model predictive adaptive controller that can handle the nonlinearities associated with the UAV was developed based on state space technique and the model predictive controller (MPC) utilizes a neural network model to envisage future plant (aircraft) responses to potential control signals. The developed model predictive controller network was successfully trained offline using Feed-forward Back-propagation algorithm with speed and position as inputs since the underlying objective of this work is to improve the speed and position in order to advance the safety collision distance of the UAV because these parameters are mostly considered in the avoidance maneuver performance. Also, the results reveal that the speed of the generated UAV is approximately the input speed of 75MS–1 during the time the UAV is affecting maneuvering. Significance of the result is that the proposed algorithm is capable of generating collision-free trajectory for different waypoint cases.

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.

Effect of Leading-Edge Cranks on Stability and Control of Active-Flow-Control-Enabled Tailless Aircraft

The Swept Wing Flow Test (SWIFT) is a tailless unmanned combat aerial vehicle (UCAV) model to be tested at high Reynolds numbers in NASA’s National Transonic Facility. The model is designed around a -shaped wing with a single, large crank at its leading edge (LE). It suffers from an unstable nose-up pitch departure resulting from flow separation augmented by the LE crank. A small-scale, modular wind tunnel model () was built that allowed for changes in the crank angle by increasing the outboard wing sweep. Eliminating the crank entirely increased the and changed the sign of pitch departure, thus exposing the significance of the LE crank. The model was equipped with sweeping jet actuators that could be individually enabled by valves located at the actuator inlets, allowing one to explore the role of active flow control (AFC) in expanding the model’s longitudinal stability margins and controlling its yaw while being cognizant of the coupling between changes in the model’s planform and their effect on AFC. Test results indicated that selective actuation depending on the model’s attitude modified the flow and dramatically increased the trimmed , while further suggesting that the actuation should dynamically change with incidence to improve AFC efficacy.

Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs

Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications. This study focuses on developing a safe reinforcement learning method for back-transition control between level flight mode and hover mode for ducted-fan tail-sitter UAVs. Our method enables transition control with a minimal altitude change and transition time while adhering to the velocity constraint. We employ the Trust Region Policy Optimization, Proximal Policy Optimization with Lagrangian, and Constrained Policy Optimization (CPO) algorithms for controller training, showcasing the superiority of the CPO algorithm and the necessity of the velocity constraint. The transition trajectory achieved using the CPO algorithm closely resembles the optimal trajectory obtained via the well-known GPOPS-II software with the SNOPT solver. Meanwhile, the CPO algorithm also exhibits strong robustness under unknown perturbations of UAV model parameters and wind disturbance.

Experimental and computational investigation of the vortical structures generated from a blended-wing-body UAV model

The current study presents a combination of experimental and computational investigations of a scaled-down Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV) model. The BWB model features highly swept wings. The experiments are conducted in a subsonic windtunnel in the Laboratory of Fluid Mechanics and Turbomachinery, in the Aristotle University of Thessaloniki. More specifically, surface oil flow visualization and Laser-Doppler Anemometry (LDA) are employed to investigate the vortical structures emanating from the leading edge and the wingtips of the model and provide the corresponding velocity and vorticity profiles. Using the windtunnel measurement data as a reference point, both steady-state and transient computational fluid dynamics (CFD) analyses are also conducted. The flow around the BWB scaled-down model is modeled by adopting four turbulence models, three of them based on the concept of the eddy-viscosity and one, on the Reynolds-stress transport equation modeling. The modeling results are compared to the experimental measurements, emphasizing on the streamwise and spanwise velocity profiles, axial vorticity profiles and separation patterns. The results of this study provide insight on the vortical phenomena dominating the flowfield over and around BWB configurations and conclusions concerning the tradeoffs between accuracy and computational efficiency are drawn.

Research and Design of VR Based Unmanned Aerial Vehicle Model and Database

In response to the application of drones in real life, drones are more susceptible to interference and influence from various external factors such as weather, site, airspace, etc. during flight operations or related tasks. Not only can they not guarantee the completion of expected goals or tasks, but they are also prone to problems such as falling, collision, or accidental injury caused by the unstable state of drones. The drone flight simulation, virtual training, and drone database system developed based on VR technology has improved the safety, diversity, and instability of drones in practical applications, and reduced the interference of external adverse factors on drone flight. A comprehensive drone model database system has been established. This provides effective guarantees for the application and implementation of drones in various fields.

Optimal trajectory planning of a small UAV using solar energy and wind energy

Due to size limitations and the working environment near the ground, small low-altitude unmanned aerial vehicles (UAVs) cannot carry large-area solar cells and energy storage devices. Solar energy alone has only a limited effect on the improvement of their endurance. To solve this problem, this study proposes the combination of solar energy technology and dynamic soaring technology to improve the endurance of small UAVs. Then, the optimal energy acquisition strategy based on the combination of solar energy and dynamic soaring technology is analyzed in terms of energy. The motion equations and energy acquisition and consumption models of a small solar UAV based on horizontal wind shear are established. Moreover, an optimal trajectory planning problem with the maximum charging power of rechargeable batteries as the optimization objective is proposed. The optimal trajectory is solved based on the hp adaptive pseudo-spectral method, and the optimal trajectory combining the two technologies is simulated and compared with the constant-altitude constant-velocity (CACV) trajectory of the UAV using only solar technology. The simulation results show that under the flight background of tmission = 6:00 (summer sunrise) and β = 0.125 s−1 (wind gradient of the wind shear), the optimized O-type trajectory decreased the energy consumption power by 39.86% and increased the solar energy acquisition power by 86.34% compared to the CACV trajectory. Consequently, the small UAV stores more energy in the rechargeable batteries to improve the endurance performance. Compared to the environment where the sun is located on the leeward side of the wind shear, the environment where the sun is located on the windward side of the wind shear can enable the UAV to acquire more energy through the optimized O-type trajectory.

Adaptive attitude control of UAV based on neural network compensation

Aiming at the problem of unmanned aerial vehicle (UAV) attitude control with insufficient robustness and anti-disturbance, this paper proposes a UAV attitude control method combining incremental nonlinear dynamic inverse (INDI) and neural network (NN) adaptive compensation. Firstly, the attitude model of UAV with time scale separation is proposed. On this basis, the INDI control law is derived, and the angular acceleration information required by INDI method is feedback by the filter. Then, the adaptive NN compensation structure is introduced to eliminate the uncertainty caused by model mismatch, sensor delay and external disturbance. Finally, the UAV simulation is carried out. The result shows that this method improves robustness and anti-disturbance of original INDI method without changing structure and parameters of controller, and has a certain adaptive compensation ability for system uncertainty.

A hybrid thrusting system for increasing the endurance time of multirotor unmanned aerial vehicles

One of the most significant disadvantages of electric multirotor unmanned aerial vehicles is their short flight time compared to fuel-powered unmanned aerial vehicles. This is mainly due to the low energy density of electric batteries. Fuel has much more energy density when compared to batteries. Electric-powered motors in multirotor unmanned aerial vehicles cannot be replaced with fuel-based engines because the stability and control of multirotor unmanned aerial vehicles rely on the high response rates of electric motors. One of the possible solutions to overcome this problem of short endurance times is by using hybrid thrusting systems that combine the advantages of both fuel and electrical propulsion systems, where high maneuverability and long endurance flight time could be achieved. In this work, hybrid thrusting and power systems for multirotor unmanned aerial vehicles are studied. Targeted hybrid thrusting systems consist of combustion engines, electric motors, and their power sources. Then a hybrid thrusting system-based quadrotor unmanned aerial vehicle model is developed. The article presents the altitude and attitude control systems of the developed hybrid thrusting system-based unmanned aerial vehicle. The presented hybrid quadcopter model comprises four electric motors and one fuel engine. The fuel engine used in this work is a 4.07 cc internal combustion engine targeting 2–3 kg unmanned aerial vehicles with up to 5 kg maximum takeoff weight. The developed hybrid quadrotor unmanned aerial vehicle achieved a 139% improvement in flight time when compared with traditional electric-based quadrotor unmanned aerial vehicles. The article also reports on other flight time-related issues such as the optimal fuel mass to battery size ratio to maximize the endurance time of the quadrotor unmanned aerial vehicles.

DEVELOPMENT OF THE METHOD FOR DETERMINING THE CONTENT OF THE SYNTHETIC DYE DIAMOND BLUE FCF IN THE SORPTION MATERIAL BY THE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY METHOD

Introduction. To date, the use of unmanned aerial vehicles (UAVs) in Ukraine for the agricultural lands treatment is a new promising technology that requires detailed study and development of approaches to risk assessment and hygienic regulation. Development of a method that will allow measuring the content of diamond blue FCF in the sorption material (filter paper) is relevant. The aim of the study – development of the method for determining the content of the synthetic dye diamond blue FCF in the sorption material by the high-performance liquid chromatography method. Research Methods. The following materials were used: laboratory analytical balance Radwag® AS220.R2, rotary evaporator, cartridge Strata™ C18-e (55 μm, 70 °С) 500 mg/6 ml, f. Phenomenex, steel chromatographic column 150/4.6 Microsorb 100-5 C18, pre-column chromatographic 4/3 Microsorb 100-5 C18, liquid chromatograph “Shimadzu” with a diode array detector, Diamond blue FCF, standard, 87.7, paper filters de-ashed “red ribbon”. Results and Discussion. At the first stage of the research, samples were taken and prepared. For analysis, 2 parallel samples were taken. The next stage was the preparation of the sample for introduction into the chromatograph. The third stage was chromatography performing. At the final stage, the diamond blue FCF peak areas were determined and calculated on the chromatograms. The indicated method of determining the content of diamond blue FCF in the sorption material (filter paper) includes extraction of the dye from the sorption material (filter paper) with distilled water; solid-phase extraction and quantification of diamond blue FCF by reversed-phase HPLC with SF detection. This method differs from the known ones in that it makes it possible to determine the investigated dye in the sorption material. Conclusion. The proposed method of containing diamond blue FCF in the sorption material (filter paper) will allow to evaluate the effectiveness and safety of the use of various models of UAVs in combination with various pesticide preparations when using different agrotechnical characteristics at the stage of pre-registration trials and scientific research.

A Dual Aircraft Maneuver Formation Controller for MAV/UAV Based on the Hybrid Intelligent Agent

This paper proposes a hybrid intelligent agent controller (HIAC) for manned aerial vehicles (MAV)/unmanned aerial vehicles (UAV) formation under the leader–follower control strategy. Based on the high-fidelity three-degrees-of-freedom (DOF) dynamic model of UAV, this method decoupled multiple-input-multiple-output (MIMO) systems into multiple single-input-single-output (SISO) systems. Then, it innovatively combined the deep deterministic policy gradient (DDPG) and the double deep Q network (DDQN) to construct a hybrid reinforcement learning-agent model, which was used to generate onboard desired state commands. Finally, we adopted the dynamic inversion control law and the first-order lag filter to improve the actual flight-control process. Under the working conditions of a continuous S-shaped large overload maneuver for the MAV, the simulations verified that the UAV can achieve accurate tracking for the complex trajectory of the MAV. Compared with the traditional linear quadratic regulator (LQR) and DDPG, the HIAC has better control efficiency and precision.

Fault Tolerant Super Twisting Sliding Mode Control of a Quadrotor UAV Using Control Allocation

In this study, a fault-tolerant super-twisting sliding mode controller with a control allocation system for a quadrotor aircraft is proposed. Super twisting sliding mode control is a robust control technique that handles a system with a relative degree equal to one. A super-twisting sliding mode controller is proposed because of its robustness to uncertainties and perturbations. It increases accuracy and reduces chattering. A control allocation algorithm is developed to cope with the actuator fault. Firstly, a nonlinear model of the quadrotor unmanned aerial vehicle (UAV) is presented. Then, the controller design and type of the actuator fault are explained. The control allocation algorithm is used to optimize the trajectory tracking performance of the quadrotor in the presence of an actuator fault. A control allocation algorithm is an effective approach to implementing fault-tolerant control. When actuator faults are identified, they can be modeled as changes in the B matrix of constraints. Various simulations have been made for situations with and without actuator failure. In normal conditions, the quadrotor can accurately track altitude, roll, pitch and yaw references. In faulty conditions, the quadrotor can follow the references with a small error. Simulations prove the effectiveness of the control allocation algorithm, which stabilizes the quadrotor in case of an actuator fault. Overall, this paper presents a novel fault-tolerant controller design for quadrotor aircraft that effectively addresses actuator faults using a super-twisting sliding mode controller and control allocation algorithm.