Model Of Quadcopter Research Articles

Trajectory Tracking Control for a Quadcopter under External Disturbances

This paper initially discusses the application of a Sliding Mode Controller (SMC) for drone control, encompassing vertical takeoff and landing. Subsequently, the dynamic model of the quadcopter is formulated using the Newton-Euler method. Despite the challenges posed by the nonlinear characteristics of Unmanned Aerial Vehicles (UAVs), empirical evidence from previous tests and simulation studies underscores the efficacy of the SMC in delivering satisfactory performance and robust resistance against interference. Moreover, this research endeavors to present a quadcopter model and simulation, leveraging the SMC alongside the Newton-Euler formula to enhance control precision in the face of external magnetic disturbances affecting the UAV. Both the position and attitude of the UAV are governed by the SMC. The dynamic and control models of the quadcopter are implemented and visualized in MATLAB, culminating in results that substantiate the efficacy of the proposed controller across diverse scenarios. Furthermore, the performance of the proposed control method is compared with alternative methodologies such as PID, particularly in scenarios involving disturbances. The simulation results indicate promising and practical implications.

Cascade PID Control for Altitude and Angular Position Stabilization of 6-DOF UAV Quadcopter

UAVs are commonly used in transportation, especially in the express delivery of light cargo parcels. However, controlling UAVs is difficult because of their complex structure and wide range of operations in space. The research contribution is proposed a cascade control structure using six PID controllers for the 6-DOF UAV quadcopter, that ensures the altitude angulars positions control at the desired values and maintains flight balance stability for the 6-DOF UAV quadcopter. First, the mathematical dynamic models for the 6-DOF UAV quadcopter have been researched and developed, including the translational dynamic mathematical model and the rotational dynamic mathematical model of the 6-DOF UAV quadcopter. This is a complex object with strong nonlinearity and difficult control. And then, the article introduces the method of designing six PID controllers for 6-DOF UAV quadcopter to meet the requirements, based on applying the Ziegler-Nichols experimental method. Applying the Ziegler-Nichols experimental method makes the process of designing a UAV quadcopter control system simple, straightforward and heuristics with fast controller parameters tuning. Next, the article presents the results of modeling and simulation of the 6-DOF UAV quadcopter control system on Matlab/Simulink. The simulation results show that the six proposed PID controllers have ensured the flight balance stability at the desired altitude and angular positions with overshoot less than 20%, steady-state error less than 1%. This shows the prospect of applying the proposed PID control method to physical UAVs, easily adjusting PID parameters to suit the flight environment.

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets.

The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research.

Mathematical modelling and PID control system implementation for quadcopter frame Tarot FY650

PurposeThe purpose of this study is to find the suitable trajectory path of the Numerical model of the Quadcopter. Quadcopters are widely used in various applications due to their compact size and ease of assembly. Because they are quite unstable, autonomous control systems would be used to overcome this problem. Modelling autonomous control is predominant as the research scope faces challenges because of its highly non-linear, multivariable system with 6 degree of freedom.Design/methodology/approachQuadcopters with antonym systems can operate in an unknown environment by overcoming unexpected disturbances. The first objective when designing such a system is to design an accurate mathematical model to describe the dynamics of the system. Newton’s law of motion was used to build the mathematical model of the system.FindingsEstablishment of the mathematical model and the physics behind a four propeller drone for the frame TAROT 650 carbon was done. Simulink model was developed based on the mathematical model for simulating the complete dynamics of the drone as well as location and gusts were included to check the stability.Originality/valueThe control response of the system was simulated numerically results are discussed. The trajectory path was found. The phases with their own parameters can be used to implement the mathematical model for another type of quadcopter model and achieve quick development.

Methodology for the Synthesis of a Multicopter Controller Acting as a Swarm Agent using the Thermal Motion Equivalent Method

The paper is dedicated to the development of a method for the synthesis of the control system of a swarm multicopter. The motion of the agents in the swarm is organised by the thermal motion equivalent method. The idea of the method is the behavioral similarity of the thermal motion of the atoms by the agents. In the practical implementation of the thermal motion equivalent method, it is important to ensure constancy of velocity and isotropy of agent dynamics. Violating these properties will cause the swarm to fail a mission, such as an area exploration, by reducing the RMS speed of the agents to zero. The proposed solution to these problems is to synthesize a modal controller for the agent-boundaries test system for the slowest control channel, thereby ensuring RMS velocity constancy of the agent. The synthesized controller is used as a filter in the fast-acting channels, the second horizontal channel and the vertical channel. In the fast-acting channels, an additional filter is proposed to bring their dynamics to the slowest channel, thereby ensuring isotropy. The inclusion of a limit on the maximum length of the equivalent field vector ensures isotropy. The synthesis was carried out using a simplified multicopter dynamics mathematical model, obtained with small UAV deviations from the vertical and without considering the Coriolis force. The methodology for the synthesis of a multicopter control system for functioning as part of a swarm is developed using the obtained results. Numerical simulation results of both a single vehicle in closed space and a swarm using a more complete nonlinear dynamic quadcopter model are presented. The proposed method has the advantage of simple synthesis using a linear model. Numerical simulation results confirm the operability of the developed methodology.

Evolvable Hardware Based Optimal Position Control of Quadcopter

Trading off performance metrics in control design for position tracking is unavoidable. This has severe consequences in mission-critical systems such as quadcopter applications. The controller area and propulsion energy are conflicting design parameters, whereas the reliability and tracking speed are related metrics to be optimized. In this research, a switching-based position controller was co-simulated with the quadcopter model. Performance analysis of the Field Programmable Gate Array (FPGA)-based controller validates a better scheme for tracking speed, propulsion energy, and reliability optimization under similar error performance. To improve the computation power and controller area, the dynamic partial reconfiguration(DPR) approach has been adapted and implemented on FPGA using the Vivado Integrated Development Environment (IDE), where a ranking-based approach brings into action either proportional derivative, sliding mode, or model predictive controllers for each dimension of position tracking. It is verified by analyzing the cumulative tracking speed, reliability, controller area, and propulsion energy metrics that the proposed controller can optimize all these metrics within three successive iterations of tracking either in the same direction or in any combination of directions. Concerning the implementation results of the controller with the switching-based controller, there is 6 % computation power and 30 % resource savings due to DPR .

Design and development of an autonomous unmanned aerial vehicle for surface coalmines surveillance

This paper presents a detailed investigation into the development of a specialized unmanned aerial vehicle (UAV) for practical applications in coal mining. The main focus is on enhancing surveillance capabilities, identifying potential hazards, and supporting various coal mine activities. This study initiates with the careful design of a quadcopter, taking into account essential parameters such as VTOL, maneuverability, and hover efficiency. The quadcopter model is meticulously developed to ensure smooth operation under ideal conditions. The design process involves utilizing SolidWorks software to create a robust and optimized quadcopter model. Additionally, the static deformation simulation of the quadcopter is conducted using Ansys software, providing valuable insights into its structural integrity and performance. Dynamic modelling using the Bond Graph Method enables a thorough understanding of the quadcopter’s behavior and informs propulsion system selection. The chosen propulsion system prioritizes maximizing thrust-to-weight ratio to about 2:1, achieving the factor of safety of approximately 4, and ensuring maneuverability. The use of eCalc, an online open-source software, assists in identifying the ideal motor-propeller combination for optimal thrust characteristics. Surveillance and mapping techniques are explored in detail. A step-by-step methodology is proposed for image identification, while mapping is achieved through image stitching and path planning using waypoints. Additionally, volume estimation and vulnerability assessment methods specific to coal mining are discussed.

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.

Design of a PEM fuel cell powered autonomous quadcopter

The purpose of this paper is to design a PID controller for a detailed nonlinear model of a custom-made quadcopter and utilise the power requirements in designing a proton exchange membrane (PEM) fuel cell system. The first principles methodology to commence with unmanned aerial vehicle architecture is delineated. The empirical actuator response of the brushless DC (BLDC) is obtained which further adds accuracy to the mathematical model of the system. The paper also presents simulations for nonlinear open loop and closed loop feedback with PID controller. The controller gains are obtained from the simulations for hover flight mode. The experimental implementation of battery powered quadcopter is validated with simulation results of PID control design. Finally, for the designed quadcopter, a mathematical model of a PEM fuel cell system to replace battery-specific power requirements is obtained. The key limitation of this paper is the absence of experimental data for fuel cell model due their high cost and low-availability.

LMI based H∞ Observer Design for a Quadcopter Model Operating in an Adaptive Vertical Farm

The adaptive vertical farm is an innovative solution to increase production yield through adaptive management of available volume. The latest technological advances in data processing and actuators have made UAVs useful in precision agriculture because of their ability to monitor small areas. This paper proposes a H∞ observer design via Linear Matrix Inequalities (LMI) aiming to provide accurate state estimation of an indoor quadrotor operating in an adaptive vertical farm. A new less conservative LMI condition is applied to solve the H∞ circle criterion design. A simulation is given to illustrate the validity and effectiveness of the proposed observer.

Finite time settling controller for a hierarchically linearized quadcopter model

In this paper, we apply finite-time settling control to a hierarchical linearized quadcopter model and verify its robustness against model errors by simulation. Hierarchical linearization is one of the exact linearization method and is linearizable on all points. Finite-time settling control has the property of finite-time stability, which perfectly matches the equilibrium point in a finite-time range. This property increases robustness near the equilibrium point. Thus, it is expected to improve the stability of drones that are vulnerable to wind disturbances and model errors. As a comparison, linear state feedback control with exponential stability is used. The feedback gain is calculated using linear quadratic regulator(LQR), which is known to have high robustness. The simulation results show that the finite-time settling controller has higher the trajectory tracking performance than the linear quadratic regulator.

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.

OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results

Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional-Integral-Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications.

Modeling and Nonlinear Control of a Quadcopter for Stabilization and Trajectory Tracking

This paper presents an adequate mathematical representation of a quadcopter’s system dynamics and effective control techniques. A quadcopter is an unmanned aerial vehicle (UAV) that is able to do vertical take-off and landing. This study presents a nonlinear quadcopter system’s mathematical modeling and control for stabilization and trajectory tracking. The mathematical model of the system dynamics of the quadcopter is derived using Newton and Euler equations with proper references to the appropriate frame or coordinate system. A PD control algorithm is developed for the nonlinear system for stabilization. Another nonlinear control technique called full state feedback linearization (FBL) using nonlinear dynamic inversion (NDI) is developed and implemented on the quadcopter system. However, there is a problem with the normal approach of the complete derivation of the full state FBL system using NDI as gathered from the literature review. In such an approach, the PD controller that was used for attitude stabilization was able to stabilize the angles to zero states, but the position variables cannot be stabilized because the state variables are not observable. Thus, a new approach where the position variables are mapped to the angle variables which are controllable so as to drive all states to zero stability was proposed in this study. The aim of the study was achieved but the downside is that it takes a longer time to achieve this stability so it is not efficient and should only be considered when absolute zero stability is the aim without considering time efficiency. The study further investigates the problem of nonlinear quadcopter system’s mathematical modelling and control for stabilization and trajectory tracking using the feedback linearization (FBL) technique combined with the PD controller. The proposed control algorithms are implemented on the quadcopter model using MATLAB and analyzed in terms of system stabilization and trajectory tracking. The PD controller produces satisfactory results for system stabilization, but the FBL system combined with the PD controller performs better for trajectory tracking of the quadcopter system.

Concept of anti-collision algorithm for unmanned aerial vehicles

The basis of the novel anti-collision algorithm is a software implementation that allows theUAV to avoid collisions with environmental obstacles, as well as with other flying objects. The paperuses simplified equations describing the dynamics of the quadcopter to facilitate the modelling ofthe simulation structure. The software implementation of the quadcopter model together with thecontroller is the basis for the operation of the anti-collision algorithm. The model control system usesa three-stage proportional-integral-differential controller. The inspiration of the resulting program ismagnetic interaction. The obstacle avoidance algorithm is based on the measurement of angular valuesand the selection of a proportional virtual force. The force repelling a quadcopter from an obstacle isa parameter that depends on its linear velocity, bearing on the obstacle and distance to the obstacle.The heat maps obtained reflect the scaling of the value and direction of the repulsive force. Afterdefining the target point and the position of the obstacle, the necessary parameters are measured andthe collision course correcting coordinates are selected on-board the quadcopter. The flight parametersof the quadcopter and the control coefficients of the anti-collision algorithm were analysed. Thecorrectness of the programs operation was checked by simulation using numerous characteristics.Keywords: Unmanned Aerial Vehicle, obstacle avoidance, anti-collision algorithm, UAV dynamicsmodel, quadcopter

A cooperative particle swarm optimization approach for tuning an MPC-based quadrotor trajectory tracking scheme

This paper presents a novel method for tuning a quadrotor trajectory-tracking model predictive control (MPC) framework, based on cooperative particle swarm optimization (PSO). The overall control strategy is decomposed into two different schemes; the first scheme, which is responsible for position control, is based on MPC, whereas the second one, which regulates the attitude of the quadrotor, makes use of three standard PID controllers. To optimize the trajectory tracking ability of the quadrotor, a cooperative PSO framework is introduced for tuning the large number of parameters of diverse nature, which are employed by the different controllers. The method makes use of two distinct swarms, with the first one containing the position controller parameters and the second one the attitude controller parameters. By exchanging information, the two swarms work together in a cooperative way in order to explore more efficiently the search space and discover tuning parameters that improve the trajectory tracking performance. Simulation results on a detailed quadcopter model over trajectories with different geometric characteristics, and comparisons to other tuning approaches verified by statistical testing, illustrate the efficiency of the proposed scheme in optimizing the control parameters. The scheme's robustness is also verified by testing the performance of the resulting controller on trajectories different than the ones used for tuning it.

Dynamics-Based Modified Fast Simultaneous Localization and Mapping for Unmanned Aerial Vehicles With Joint Inertial Sensor Bias and Drift Estimation

In this paper, the problem of simultaneous localization and mapping (SLAM) using a modified Rao Blackwellized Particle Filter (RBPF) (a modified FastSLAM) is developed for a quadcopter system. It is intended to overcome the problem of inaccurate localization and mapping caused by inertial sensory faulty measurements (due to biases, drifts and noises) injected in the kinematics (odometery based) which is commonly used as a motion model in FastSLAM approaches. In this paper, the quadcopter’s dynamics with augmented bias and drift models is employed to eliminate these faults from the localization and mapping process. A modified FastSLAM is then developed in which both Kalman Filter (KF) and Extended Kalman Filter (EKF) algorithms are embedded in a PF with modified particles weights to estimate biases, drifts and landmark locations, respectively. In order to make the SLAM process robust to model mismatches due to parameter uncertainties in the dynamics, measurements are incorporated in the PF and in the particle generation process. This leads to a cascaded two-stage modified FastSLAM in which the extended FastSLAM 1.0 (to include dynamics and sensory faults) is employed in first stage and the results are used in second stage in which probabilistic inverse sensor models are incorporated in the particle generation process of the PF. The efficiency of the proposed approach is demonstrated through a co-simulation between MATLAB-2019b and Gazebo in the robotic operating system (ROS) in which the quadcopter model is simulated in Gazebo in ROS using a modified version of the Hector quadcopter ROS package. The collected pointcloud data using LiDAR is then utilised for feature extraction in the Gazebo. The simulation environment used to this aim is validated based on experimental data.

ЭКСПЕРИМЕНТАЛЬНЫЕ ИССЛЕДОВАНИЯ ТОЧНОСТИ ГЕОДЕЗИЧЕСКОГО МОНИТОРИНГА ВЕРХНЕЙ ПОВЕРХНОСТИ ИНЖЕНЕРНЫХ ОБЪЕКТОВ И СООРУЖЕНИЙ

The development of totally new methods for geodetic monitoring of engineering objects and con-structions in emergency state is a relevant scientific and technical task of geodesy. The results of such studies allow to ensure prompt and reliable data of the condition of a spatial object for the purpose of its safe exploitation. The article presents experimental studies of a quadcopter model according to the developed by the author method for geodetic monitoring of engineering objects and constructions based on the multi-agent system theory. The article describes the test model of quadcopter and mean square error calculation of elevation measurement in the laboratory conditions of the station. Based on the experiment results the article makes the conclusion about the possibility of measurement by the described method. The suggested method is possible in situations when measurements by man are im-possible and the object of geo-monitoring is an inaccessible place.

Model Predictive Controller for Quadcopter Trajectory Tracking Based on Feedback Linearization

In this study, a feedback linearization model predictive control algorithm is designed for quadcopter trajectory tracking. By applying feedback linearization to the quadcopter nonlinear model, the quadcopter nonlinear model is transformed into a linear system as the foundation for further controller design. By feedback linearization, the linear control schemes can be used to control the quadcopter. A model predictive controller is then designed for the linearized quadcopter model without considering the external disturbance. A disturbance observer is designed to estimate the external disturbance, keeping the estimation error BIBO stable to compensate for the external disturbance. Numerical simulations are performed to evaluate the proposed control algorithm. The simulations are performed in two test scenarios to examine the tracking performance. The quadcopter is commended for reaching a waypoint (scenario. I) and tracking a helical trajectory (scenario. II) in the simulations, and the root means square errors are calculated to demonstrate the tracking effectiveness. The simulation results show that the designed control algorithm can effectively ensure the quadcopter tracks a given trajectory under constant or continuous disturbances.

Nonlinear Modeling the Quadcopter Considering the Aerodynamic Interaction

This paper presents a nonlinear model of the quadcopter to simulate the actual motion. An aerodynamic model of the rotor is obtained through wind-tunnel tests, which consider the variations in the rotor’s angular velocity, incoming flow speed, and angle of attack of the rotor ( $\alpha _{p}$ ). The quadcopter’s fuselage aerodynamics and mutual interference are simulated using the computational fluid dynamic (CFD) method. The mesh motion method is used to simulate the rotation of the rotor. It can be found that the interference has a significant impact on the aerodynamic load of the fuselage and the thrust and pitch moment of the rear rotor. The mathematical expression of the interference model is given by adopting the idea of the small disturbance hypothesis. The yaw dynamics model is also improved by considering the motor’s dynamics. The quadcopter’s nonlinear model can be obtained by combining the rotor model, fuselage model, motor model, and interference model. Then, flight experiments are done at hovering and forward flight states to verify the established model, and the experiment results are in good agreement with the nonlinear model. Finally, the quadcopter’s characteristic descriptive abilities of different accurate models are discussed to help designers to build a suitable quadcopter model according to the requirements.