Quadcopter Unmanned Aerial Vehicle Research Articles

Tilt-X: Development of a Pitch-Axis Tiltrotor Quadcopter for Maximizing Horizontal Pulling Force and Yaw Moment

In recent years, there has been a significant amount of research on tiltrotor multicopter unmanned aerial vehicles (TM-UAVs) in aerial robotics. Despite the varying frame types of TM-UAVs, they all still aim to decouple the propeller from the body, which means that the propeller’s attitude control is independent of the body’s attitude control. On the one hand, this solves the issue of multicopter unmanned aerial vehicles (M-UAVs) being limited by small roll and pitch angles, thereby improving flight performance. On the other hand, it addresses the drawbacks of M-UAVs as typical underactuated systems. However, the fact still remains that it cannot significantly change thrust direction, thus providing the necessary wrench direction for aerial manipulation. This paper presents a pitch-axis tiltrotor quadcopter unmanned aerial vehicle (UAV) design named Tilt-X, which can maximize horizontal pulling force and yaw moment when used as an aerial manipulator. This design contributes to tasks such as pushing, pulling, and twisting. The reliability of the design has been demonstrated through dynamic modeling and experimental validation.

An optimal hybrid quadcopter control technique with MPC-based backstepping

Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter’s position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov’s theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the �� and �� positions and 0.15 m for the �� position.

Attitude modelling and real-time robust control of a 3-DoF quadcopter UAV test bench

Abstract In this work, a three degrees-of-freedom (3-DoF) static quadcopter unmanned aerial vehicle (UAV) test-rig of a pendulum-type configuration is custom-designed, developed, instrumented, and interfaced with a PC. The rig serves as a test bed to develop high-fidelity mathematical models as well as to investigate autopilot designs and real-time closed-loop controllers’ performances. The Simulink Desktop Real-Time software is employed for the quadcopter’s attitude signals acquisition and real-time implementation of closed-loop controllers on a target microcontroller hardware. The mathematical models for pitch, roll, and yaw axes are derived via the first principle and validated with the experimental linear system identification (SI) techniques. Subsequently, employing the multi-parameter root contour technique, the classical proportional integral derivative (PID) controllers are designed and implemented in real-time on the quadcopter UAV test rig. This served as a benchmark controller for comparing it with an integral-based linear quadratic regulator (LQR) controller. Further, to improve the transient response of the LQR controller, a novel robust integral-based LQR controller with a feedforward term (LQR-FF) is implemented, which shows much superior performance than the benchmark and basic LQR controller. This work thus will act as a precursor for a more complex 3-DoF autopilot design of an untethered quadcopter.

Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

Swarming is one of the important trends in the development of small multi-rotor UAVs. The stable operation of UAV swarms and air-to-ground cooperative operations depend on precise relative position information within the swarm. Existing relative localization solutions mainly rely on passively received external information or expensive and complex sensors, which are not applicable to the application scenarios of small-rotor UAV swarms. Therefore, we develop a relative localization solution based on airborne monocular sensing data to directly realize real-time relative localization among UAVs. First, we apply the lightweight YOLOv8-pose target detection algorithm to realize the real-time detection of quadcopter UAVs and their rotor motors. Then, to improve the computational efficiency, we make full use of the geometric properties of UAVs to derive a more adaptable algorithm for solving the P3P problem. In order to solve the multi-solution problem when less than four motors are detected, we analytically propose a positive solution determination scheme based on reasonable attitude information. We also introduce the maximum weight of the motor-detection confidence into the calculation of relative localization position to further improve the accuracy. Finally, we conducted simulations and practical experiments on an experimental UAV. The experimental results verify the feasibility of the proposed scheme, in which the performance of the core algorithm is significantly improved over the classical algorithm. Our research provides viable solutions to free UAV swarms from external information dependence, apply them to complex environments, improve autonomous collaboration, and reduce costs.

Unmanned Aerial Vehicle Position Estimation Augmentation Using Optical Flow Sensor

Flight control of unmanned aerial vehicle (UAV) requires reliable measurements of UAV’s position and attitude. Optical flow sensor is able to detect UAV’s motion with respect to the ground, and thus it can be incorporated in the integrated navigation system to enhance the positioning accuracy. However, the commonly used measurement model of optical flow sensor is actually fit for continuous-time condition only, since it defines the optical flow as the instantaneous velocity of a pixel on the image plane. For commercial optical flow sensors that work under discrete-time condition, a novel measurement model is proposed in this paper, which gives a vectorized symmetrical description of the optical flow measurement between every two successive frames in the image sequence. Moreover, a cubature transform-based data fusion scheme is also presented in this paper, which can directly augment the UAV’s position estimation with optical flow data, rather than extracting UAV’s velocity information from optical flow for dead-reckoning, and hence it can be easily added to the UAV’s navigation system without changing the existing algorithm flow. Flight tests are conducted using a quadcopter UAV that equipped with PIXHAWK autopilot and PX4Flow optical flow sensor, and the test results prove that the proposed optical flow model and data fusion scheme can effectively improve the accuracy of UAV position estimation in various outdoor environments.

Novel gain-tuning for sliding mode control of second-order mechanical systems: theory and experiments

The sliding mode control is well-known as a useful control technique that can be applied in several real-world applications. However, a straightforward and efficient process of selecting the sliding mode control gains remains a challenging but interesting topic. This paper investigates a novel gain tuning method for the sliding mode control of second-order mechanical systems. Firstly, we obtain relations between the gains and the natural and damping ratio of the closed-loop system. Secondly, the time constant of the system’s actuators and the system response performance criteria, including settling time and delay time, are taken into consideration to determine appropriate ranges of the gains. These gain ranges allow control designers to select the controller gains in a time-saving manner and ensure that the desired system performance is met and the actuators work properly. Finally, the proposed method is applied to the gain tuning process of a sliding mode altitude controller for an actual quadcopter unmanned aerial vehicle. Simulation and experimental results demonstrate the applicability and effectiveness of this method.

Robust Vision-Based Sliding Mode Control for Uncooperative Ground Target Searching and Tracking by Quadrotor

In this paper, the problem of searching and tracking uncooperative and unidentified mobile ground target using a quadcopter unmanned aerial vehicle (QUAV) is investigated. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach, combined with the virtual camera concept and robust control. This strategy makes use of the rough prior information of the target, in contrast with existing strategies, which will alleviate the problem of altitude estimation noise and enhance the overall accuracy. Hence, a new vision-based sliding mode controller (SMC) is designed to control the quadcopter taking into account the flight phase’s heterogeneity, the external disturbances and parameters uncertainties as well as the target maneuverability. In order to get a better insight about the SMC tuning and adjustment, three different reaching laws are evaluated and compared. The proposed controller allows an automatic execution of the flight strategy whilst the searching phase relies on the Camera Coverage Area (CCA) technique. The vision-based technique allows an automatic QUAV altitude tuning for optimal target observation and tracking. Another contribution of this work is the fact that the designed controller validity and stability overspan the entire scenario to reach the universal and to smoothen out surges generated by control switching. Numerical simulations are conducted to compare the proposed SMC controllers and validate the effectiveness of the whole strategy.

Nonlinear adaptive sliding mode control with application to quadcopters

AbstractNonlinear adaptive sliding mode control (NASMC) has the capability to adequately control a system whose parameters are unknown to the controller designer. Conventional model-based controllers require a mathematical dynamic model of the system with known parameters. These system parameters are normally determined by extensive system identification experiments, which are expensive and time-consuming to perform. A NASMC approach that does not require known system parameters is proposed. Using NASMC, a controller designer can skip the expensive and time-consuming system parameter identification and fast-forward to the control implementation. In addition, once a controller is derived for a quadcopter using NASMC, the same controller will work on any quadcopter with the same equations of motion but different dynamic parameters. The formulation of the NASMC is presented for general second-order and fourth-order systems. Then, as an implementation example, the application of the general NASMC approach is demonstrated by applying it to a quadcopter unmanned aerial vehicle in simulation.

Control Tuning for the Quadcopter Unmanned Aerial Vehicle Based on Genetic Evolutionary Algorithm

The quadcopter is an unmanned aerial vehicle that has been used in civil and military applications because of its relatively low cost, simple design, and ease of maintenance in addition to its unique flying characteristics and vast potential. Six Proportional-Integral-Derivative (PID) controllers are usually built for the position and attitude control of the quadcopter. However, tuning the controllers’ parameters poses a real challenge, as the system is highly coupled and contains two cascaded control loops while being underactuated (only four control inputs for the six degrees of freedom). In this study, a quadcopter model has been implemented by using MATLAB / SIMULINK where the model has been constructed to be integrated with the evolutionary genetic algorithm in addition to a third-party toolbox that is used to simulate fractional order calculus. The gains of the traditional and the fractional-order proportional-integral-derivative controllers have been tuned by using the genetic algorithm against different path shapes. The performance of the manually and the genetic algorithm tuned proportional-integral-derivative controllers have been compared with the genetic algorithm tuned fractional-order proportional-integral-derivative controllers. The genetic algorithm tuned fractional-order proportional-integral-derivative controllers have been superior and more robust in all cases with a lower fitness function value, much smother responses, and higher trajectory tracking ability.

‘Eye in the sky’: Off-the-shelf unmanned aerial vehicle (UAV) highlights exposure of marine turtles to floating litter (FML) in nearshore waters of Mayo Bay, Philippines

Litter is a serious threat to the marine environment, with detrimental effects on wildlife and marine biodiversity. Limited data as a result of funding and logistical challenges in developing countries hamper our understanding of the problem. Here, we employed commercial unmanned aerial vehicle (UAV) as a cost-effective tool to study the exposure of marine turtles to floating marine litter (FML) in waters of Mayo Bay, Philippines. A quadcopter UAV was flown autonomously with on-board camera capturing videos during the flight. Still frames were extracted when either turtle or litter were detected in post-flight processing. The extracted frames were georeferenced and mapped using QGIS software. Results showed that turtles are highly exposed to FML in nearshore waters. Moreover, spatial dependence between FML and turtles was also observed. The study highlights the effectiveness of UAVs in marine litter research and underscores the threat of FML to turtles in nearshore waters.

Unmanned aerial vehicle design for disaster victim search and rescue operation using wireless sensor network localization

<span data-sheets-value="{"1":2,"2":"The search for disaster victims carried out by search-and-rescue (SAR) team mainly uses traditional methods, which are considered to take much time and effort, and pose a high risk for the search team and the victim. Based on this problem, we conducted research to assist disaster victim search. In this research, we designed a system using passive infrared (PIR) sensors to detect and measure the direction angle of the victim. Given the direction angle from different observation points’ perspectives, we determine the victim’s position using the triangulation method. we also designed a quadcopter unmanned aerial vehicle (UAV) to carry this sensor system across the disaster area. For monitoring purposes, a local website was designed to display data generated by the system. Based on test results, the system can determine a victim’s position with a distance difference of less than 5 m. The system can search victim in an empty land ±35 m×15 m wide in 14 minutes 20 seconds. The data monitoring system also displayed the victim’s position, the position of the observation points, and the UAV’s flight path."}" data-sheets-userformat="{"2":13249,"3":{"1":0},"9":0,"10":1,"11":3,"12":0,"15":"Nunito Sans","16":9}">The search for disaster victims carried out by search-and-rescue (SAR) team mainly uses traditional methods, which are considered to take much time and effort, and pose a high risk for the search team and the victim. Based on this problem, we conducted research to assist disaster victim search. In this research, we designed a system using passive infrared (PIR) sensors to detect and measure the direction angle of the victim. Given the direction angle from different observation points’ perspectives, we determine the victim’s position using the triangulation method. we also designed a quadcopter unmanned aerial vehicle (UAV) to carry this sensor system across the disaster area. For monitoring purposes, a local website was designed to display data generated by the system. Based on test results, the system can determine a victim’s position with a distance difference of less than 5 m. The system can search victim in an empty land ±35 m×15 m wide in 14 minutes 20 seconds. The data monitoring system also displayed the victim’s position, the position of the observation points, and the UAV’s flight path.</span>

High-Gain Dual-Mode Cylindrical Rectangular Patch Antenna for Airborne Applications

This article presents a conformal antenna system for providing a dual-band high-gain communication link between an unmanned aerial vehicle (UAV) and a ground station where the two bands can be used independently for command-control and data link. A cylindrically conformal rectangular patch antenna operating in the higher order TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> and TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">03</sub> modes is proposed, which produces high gain in both the frequency bands. This is possible by loading the cylindrical–rectangular patch antenna by a centrally located slot, which makes the radiation pattern of the TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> mode broadside radiating and lowers the sidelobe level of the TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">03</sub> mode. Multiport network modeling (MNM) of the structure is done, which leads to the optimum design, and the effect of curvature on antenna performance is also studied. As the antenna has around 60° beamwidth in one of the planes for both the frequency bands, three such antennas are placed on one arm of a quadcopter UAV so that 180° coverage can be obtained by switching the antennas. The antenna system has a simulated gain of 7.94 and 10.21 dBi at 10.288 and 12.412 GHz, respectively. A single slot-loaded cylindrical–rectangular patch antenna is fabricated and the measured results are compared with those obtained by MNM and simulation. The antenna gives a gain of 7.46 and 9.83 dBi at the frequencies of 10.323 and 12.46 GHz, respectively.

Bearing-Based Distributed Formation Control of Unmanned Aerial Vehicle Swarm by Quaternion-Based Attitude Synchronization in Three-Dimensional Space

Most of the recent research on distributed formation control of unmanned aerial vehicle (UAV) swarms is founded on position, distance, and displacement-based approaches; however, a very promising approach, i.e., bearing-based formation control, is still in its infancy and needs extensive research effort. In formation control problems of UAVs, Euler angles are mostly used for orientation calculation, but Euler angles are susceptible to singularities, limiting their use in practical applications. This paper proposed an effective method for time-varying velocity and orientation leader agents for distributed bearing-based formation control of quadcopter UAVs in three-dimensional space. It combines bearing-based formation control and quaternion-based attitude control using undirected graph topology between agents without the knowledge of global position and orientation. The performance validation of the control scheme was done with numerical simulations, which depicted that UAV formation achieved the desired geometric pattern, translation, scaling, and rotation in 3D space dynamically.

Failure Detection in Quadcopter UAVs Using K-Means Clustering.

We propose an unmanned aerial vehicle (UAV) failure detection system as the first step of a three-step autonomous emergency landing safety framework for UAVs. We showed the effectiveness and feasibility of using vibration data with the k-means clustering algorithm in detecting mid-flight UAV failures for that purpose. Specifically, we measured vibration signals for different faulty propeller cases during several test flights, utilizing a custom-made hardware system. After we made the vibration graphs and extracted the data, we investigated to determine the combination of acceleration and gyroscope parameters that results in the best accuracy of failure detection in quadcopter UAVs. Our investigations show that considering the gyroscope parameter in the vertical direction (gZ) along with the accelerometer parameter in the same direction (aZ) results in the highest accuracy of failure detection for the purpose of emergency landing of faulty UAVs, while ensuring a quick detection and timely engagement of the safety framework. Based on the parameter set (gZ-aZ), we then created scatter plots and confusion matrices, and applied the k-means clustering algorithm to the vibration dataset to classify the data into three health state clusters—normal, faulty, and failure. We confirm the effectiveness of the proposed system with flight experiments, in which we were able to detect faults and failures utilizing the aforementioned clusters in real time.

Quadcopter Main Board Design with PID Algorithm as Controller

Kontes Robot Terbang Indonesia (KRTI) Flight controller division has a mission to independently make an unmanned aerial vehicle (UAV) controller. UAV controllers are usually made by non-local manufacturers, who have a package between the controller and the aircraft. Due to limited pins and specific functions, most controllers cannot adapt to user requirements. For this reason, in this study, we made a UAV controller with a more significant number of pins that can be programmed independently. Main Controller is a master board device used to control the stability of the UAV when flying. The main controller has been designed specifically for certain products so that the UAV cannot be modified. In this study, it is proposed to make the main controller that is specifically designed to fly a Quadcopter UAV. Movement control for quadcopter stability when flying using the Proportional Integral Derivative (PID) method. The main controller has a function that is to regulate the entire course of flight on unmanned vehicles. PID control can adapt to changes so that the system becomes stable. The PID control on the Main Controller is intended to balance the quadcopter concerning its orientation angle. The Main Controller is also tasked with recording data from sensors such as the Inertial Measurement Unit (IMU) and reading data from the remote. The controller successfully flew the quadcopter with a P value of 0.245, an I value of 0.0139, and a D value of 0.0085.

Quadcopter UAVs Extended States/Disturbance Observer-Based Nonlinear Robust Backstepping Control.

A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method.

Flight Control System Simulation for Quadcopter Unmanned Aerial Vehicle (UAV) based on Matlab Simulink

Unmanned aerial vehicle (UAV) are widely used in military and civilian applications due to its advantages of strong maneuverability. Flight control system simulation of a UAV is always a research focus. At present, there are three major algorithms for a UAV flight control: strapdown inertial navigation system, Kalman filter algorithm, flight control Proportional-Integral-Derivative (PID) algorithm. In this study, UAV flight control system and quadcopter drone flight control principle are discussed. In addition, this research developed a dynamic model based on a thorough examination of the quadcopter drone’s frame construction and features. This dynamic model describes the transformation matrix of UAV coordinate system to ground coordinate system. Matlab Simulink was used to simulate a flight control system. A quadcopter drone’s vertical movement, yaw motion, pitching motion, and roll motion, as well as its flight attitude, were simulated and described. Dynamic modeling is developed based on the drone’s flight principle and force relationship in different motion states, as well as the Newton-Eulerian model. Matlab Simulink was used to simulate the trajectory of a quadcopter drone. The red line denotes the trajectory, while the black x markings denote a change in trajectory or a specific position. Blue lines that reflect the waypoint’s heading accompany specific poses. The results of this study could provide a good reference for the simulation of a UAV flight control system.

ACCURACY ASSESSMENT OF 3D PHOTOGRAMMETRIC MODEL FROM UNMANNED AERIAL VEHICLE SYSTEM

Unmanned aerial vehicle (UAV) or well known as a drone is a common technique that has been used in the geospatial field for various applications as well as in three-dimensional modelling application. The UAV is an aerial vehicle that does not carry a human pilot, fly remotely or autonomous and able to carry a sensor to collect data. Nowadays, UAV has been widely used in three-dimensional modelling application around the world. It can model an accurate object in a three-dimensional view; besides that, it also can offer a cheaper solution than other techniques or systems. Therefore, the objectives of this research are to evaluate the accuracy assessment of the 3D building model that is generated from a non-metric camera integrated with a multi-rotor UAV system and also to investigate the effect of camera angle toward the quality of the 3D building model. Block T06 of the Faculty of Built Environment and Surveying was selected as a study area in this study. This building is under the management of Universiti Teknologi Malaysia, located in the district of Skudai, Johor. The quadcopter UAV system has been used to capture the high resolution of the building's aerial image from two different perspectives: nadir and oblique. Each set of aerial images were processed using photogrammetric software for producing a three-dimensional model of Blok T06. Two types of 3D building model have produced in this study which are 3D model-based nadir and oblique aerial photo and 3D model based on nadir aerial photo. Ground control point (GCP) and check point (CPs were established in the study area using the GPS rapid-static technique. Later, the processing of aerial triangulation and accuracy assessment were implementing using these points. A statistical equation was used for evaluating the quality of the three-dimensional model was generated in this research. 3D building model based on nadir and oblique aerial photo is more accurate with RMSE ±1.008m compared 3D building model-based nadir aerial photo with RMSE ±1.145m. In conclusion, the 3D building model of block T06 was successfully produced from UAV data based on UAV photogrammetry and the angle of camera also contributes towards the quality of 3D model in aerial photogrammetry.

Integral Adaptive Sliding Mode Control for Quadcopter UAV Under Variable Payload and Disturbance

The quadcopter unmanned aerial vehicle (UAV) system is considered a good platform for control scheme design as it is highly nonlinear with coupled dynamics and an under-actuated system. Considering these challenges, this manuscript aimed to propose a control algorithm to control the quadcopter system in the presence of uncertainty and disturbance influences. The integral adaptive sliding mode control scheme has been proposed to control the system. The proposed control scheme is composed of the outer loop controller to control the position of the quadcopter, while the inner loop controls the attitude of the quadcopter. The proposed control law has three major terms, firstly the equivalent control which is developed based on the Lyapunov approach to handle most of the uncertainty and disturbance, secondly the adaptive switching gain, which is achieving fast adaptation against uncertainty, finally the switching function which has been approximated by a tangent hyperbolic function to reduce the unwanted chattering phenomena. The proposed control scheme and its performance have been investigated via a MATLAB/Simulink. The results prove that the implemented control scheme is robust even in the presence of uncertainties and disturbance and the quadcopter tracks the predefined trajectories with limited chattering influence.

Quadcopter Trajectory Tracking in the Presence of 4 Faulty Actuators: A Nonlinear MHE and MPC Approach

This letter proposes an Active Fault Tolerant Control (AFTC) framework for a quadcopter Unmanned Aerial Vehicle (UAV). The proposed framework is capable of achieving trajectory tracking, estimating states, as well as estimating and compensating for all four actuators multiplicative faults. The framework combines two optimization-based nonlinear estimation and control techniques; Nonlinear Moving Horizon Estimation (NMHE) and Nonlinear Model Predictive Control (NMPC). We formulate the NMHE algorithm such that it simultaneously estimates the states and the 4 actuators faults. These estimates are then provided to NMPC to achieve fault accommodation. To validate our proposed framework, we investigate two fault scenarios, where 4 actuators experience an abrupt and time-varying partial Loss Of Effectiveness (LOE) in the presence of noisy measurements. Finally, simulation results show satisfactory performance and prove that the proposed framework is numerically tractable and real-time applicable.