UAV Control System Research Articles

Based on graph theory, design of distributed control system for multi-quadcopter UAVs

This paper addresses the formation control system for multi-quadcopter UAVs, proposing a control strategy based on finite-time stability theory. It designs position controllers with a dual power structure and attitude controllers combined with non-singular terminal sliding mode to achieve fast convergence and high-precision tracking. Through Lyapunov stability analysis, the paper proves the finite-time convergence of the control strategy. Simulation experiments validate the method's excellent performance in formation establishment, maintenance, and complex trajectory tracking, demonstrating rapid convergence, high precision, and strong disturbance rejection capabilities. This research provides theoretical and technical support for cooperative control of multi-quadcopters.

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

This study focuses on the creation and examination of an intelligent automated control system for UAVs utilized in meteorological measurements based on the Internet of Things (IoT) and mobile technologies. The proposed system enables the achievement of commendable flight control standards for UAVs during meteorological data gathering, thereby markedly enhancing the overall effectiveness of meteorological stations. Notably, this system is constructed on the foundation of three integrated principles: (a) a hierarchical two-level approach for control and data collection based on IoT and mobile technologies, (b) a straightforward and dependable fuzzy logic control characterized by high performance, and (c) the effective optimization of fuzzy control components through the application of bio-inspired multi-agent computing techniques. To assess the performance of the suggested intelligent system, this study involves the creation and bioinspired optimization of the climb speed fuzzy controller. Subsequent simulation experiments are conducted to evaluate the automatic control of UAV’s flight processes under different modes. The analysis of the simulation results indicates that the developed system, utilizing fuzzy control, exhibits superior efficiency and higher quality metrics when compared to existing counterparts, especially in diverse flight scenarios such as uniform climbing, gradual approach to designated altitude levels, and smooth landing during meteorological measurements.

MODELS OF THE SYSTEM OF COLLECTIVE SELF-ORGANISATION OF UNMANNED AERIAL VEHICLES USING ARTIFICIAL INTELLIGENCE

The article examines the current state and trends in the development of unmanned aerial vehicles (hereinafter - UAVs), methods and means of their control, self-organisation, flight routing, as well as the involvement of artificial intelligence technologies in the UAV teams system. The main trends in the use of UAVs in the defence sector are analysed. The key problems in this area are highlighted, as well as the shortcomings of existing methods and systems. The article analyses scientific works of domestic and foreign scholars, studies problematic issues and suggests ways of their solution. The introduction of artificial intelligence into the UAV control system has significant potential and makes the development of these technologies urgent. The development of UAV collective control systems, including those using artificial intelligence, allows for the effective use of technology in various fields, ensuring increased coordination, functionality and overall efficiency. At the same time, despite active research in this area, a number of problems related to the development of methods and algorithms for group work still remain unresolved. The issue of integrating information technologies created on their basis and the specifics of their implementation in collective intelligence systems in order to increase the efficiency of solving complex formalised tasks is not sufficiently studied. A method of self-organisation of the UAV team with decentralised control is proposed, in which a number of functions (route planning, distribution of roles, determination of optimal actions, obtaining and processing information) assigned to the onboard control system of the robotic air complex can be performed by each element of the UAV team system due to their self-organisation. The practicality of this method lies in the fact that the UAV's artificial intelligence will constantly self-learn and improve, and if necessary, can be reprogrammed to meet the required conditions of the task. As a consequence, the results and time required to complete missions will improve significantly, while the number of control operators will decrease. It solves a number of problems and shortcomings related to the organisation of the management system, route planning, distribution of roles, speed and completeness of receiving, processing and transmitting information, which in turn improves the security and performance of the system.

Design of fuzzy PID control system for quad-rotor UAV based on particle swarm algorithm under wind disturbance conditions

This project proposes an intelligent control method that employs a particle swarm algorithm to optimize the rules of a fuzzy controller. The rules of the fuzzy controller are continuously adjusted based on feedback data on the attitude changes of a quadrotor unmanned aerial vehicle and the enhanced particle swarm algorithm. This enables the controller to learn autonomously, thereby enhancing its performance under various conditions. In addition to optimizing the rules of the fuzzy controller, an analysis and modeling process is conducted for the characteristics of wind speed changes under natural conditions. The resulting model is introduced into the system as environmental noise, thereby improving the controller’s performance under different conditions. Experiments are conducted in the Matlab/Simulink simulation environment to test the performance of the control algorithm. The algorithm’s anti-disturbance capability and control accuracy are compared when facing complex disturbances. This research methodology offers new possibilities for precise control of quadrotor unmanned aerial vehicles and provides valuable references for future drone technology development.

Evaluation of the Success of Simulation of the Unmanned Aerial Vehicle Precision Landing Provided by a Newly Designed System for Precision Landing in a Mountainous Area

Unmanned aerial vehicle technology is the most advanced and helpful in almost every area of interest in human work. These devices become autonomous and can fulfil a variety of tasks, from simple imaging and obtaining data to search and rescue operations. The most challenging environment for search and rescue operations is the mountainous area. This article is devoted to the theoretical description and simulation tests of a prototype method of landing the light and the medium-weight UAVs used as supplementary devices for SAR (search and rescue) and HEMS (helicopter emergency medical service) in hard-to-reach mountainous terrains. The autonomous flight of a UAV in mountainous terrain has many specifics, and it is usually performed according to predetermined map points (pins) uploaded directly into the control software of the UAV. It is necessary to characterise each point flown on the chosen flight route line in advance and therefore to know its exact geographical coordinates (longitude, latitude and height of the point above the terrain), and the control system of UAV must react to the change in the weather and other conditions in real time. Usually, it is difficult to make this forecast with sufficient time in advance, mainly when UAVs are used as supplementary devices for the needs of HEMS or MRS (mountain rescue service). The most challenging phase is the final approach and landing of the UAV, especially if a loss of GNSS (global navigation satellite system) signal occurs, like in the determined area of the Little Cold Valley in the Slovak High Tatras—which is infamous for the widespread loss of GNSS signals or communication/controlling connection between the UAV and the pilot-operator at the operational station. To solve the loss of guidance, a new method for guiding and controlling the UAV in its final approach and landing in a determined area is tested. An alternative landing navigation system for UAVs in a specific mountainous environment—the authors’ designed frequency Doppler landing system (FDLS)—is briefly described but thoroughly tested with the help of artificial intelligence. An estimation of dynamic stability is used based on the time recording of the current position of the UAV, with the help of a frequency-modulated or amplitude-modulated signal based on the author’s prototype of a precision landing system designed for mountainous terrain. This solution could overcome the problems of GNSS signal loss. The presented research primarily evaluates the success of the simulation flights for the supplementary UAV. The success of navigating the UAV to land in the mountainous environment at an exact landing point using the navigation signals from the FDLS was evaluated at more than 95%.

Simulation and analysis of stability of UAV control system based on PID control under different wind forces

The four-rotor UAV has attracted the attention of various fields and has been applied in more areas due to its advantages of low cost, high reliability and simple structure. Nowadays, more complex environment and interference factors put forward higher requirements for the accuracy and stability of the flight control system of UAV. At present, the most widely used four-rotor UAV flight control system is PID control system. This paper constructs a UAV control system based on PID algorithm to analyse and study the dynamic stability control effect of UAV under different wind impact scenarios. MATLAB is used to simulate and analyze the system, and the future development is expected according to the simulation results.

Performance comparison and analysis of traditional PID and fuzzy PID control applied to UAV

Quadrotor UAVs are gradually being used in various fields because of their flexibility, ease of control and low cost. Along with advancement of electrical technology in contemporary times and increasing application scenarios of the aircraft, the demand for control systems has also increased. In different application scenarios, there are some differences in the functions and application requirements of UAV. Therefore, it is of great value to adjust the control algorithm by combining the scenario requirements. PID heuristic and fuzzy PID heuristic are the two most common control algorithms in UAV. In this paper, the heuristics are designed and compared for the above two algorithms in different scenarios and environments. First, the development process and status of UAVs are explained, and the importance of the control system is introduced. Subsequently, two control systems are studied and designed separately, and targeted comparison tests are conducted. Finally, the performance and applicability scenarios of the two control systems are analysed according to the experimental results, which in turn provide an important reference basis for the design of PID-based UAV control systems.

Multi-Tiered CNN Model for Motor Imagery Analysis: Enhancing UAV Control in Smart City Infrastructure for Industry 5.0

The concept of brain-controlled UAVs, pioneered by researchers at the University of Minnesota, initiated a series of investigations. These early efforts laid the foundation for more advanced prototypes of brain-controlled UAVs. However, BCI signals are inherently complex due to their nonstationary and high-dimensionality nature. Therefore, it is crucial to carefully consider both feature extraction and the classification process. This study introduces a novel approach, combining a pretrained CNN with a classical neural network classifier and STFT spectrum, into a Multi-Tiered CNN model (MTCNN). The MTCNN model is applied to decode two-class Motor Imagery (MI) signals, enabling the control of UAV up/down movement. The experimental phase of this study involved four key experiments. The first assessed the MTCNN model's performance using a substantial dataset, resulting in an impressive classification accuracy of 99.1%. The second and third experiments evaluated the model on two different datasets for the same subjects, successfully addressing challenges associated with inter-subject and intra-subject variability. The MTCNN model achieved a remarkable classification accuracy of 99.7% on both datasets. In a fourth experiment, the model was validated on an additional dataset, achieving classification accuracies of 100% and 99.6%. Remarkably, the MTCNN model surpassed the accuracy of existing literature on two BCI competition datasets. In conclusion, the MTCNN model demonstrates its potential to decode MI signals associated with left- and right-hand movements, offering promising applications in the field of brain-controlled UAVs, particularly in controlling up/down movements. Furthermore, the MTCNN model holds the potential to contribute significantly to the BCI-MI community by facilitating the integration of this model into MI-based UAV control systems.

Path Following for UAV using Nonlinear Model Predictive Control

For the fixed-wing UAV control system, the lateral path-following system is an important part, which ensures that the UAV flies stably in accordance with the predetermined route within a certain height. For the lateral path following of UAV based on nonlinear model predictive control, the model of the lateral kinematics of the fixed-wing UAV is established first, and then the path-following problem of UAV at a constant height is considered. The objective function with constraints based on the NMPC is established, and finally the nonlinear optimization algorithm is used to minimize the designed objective function. The optimal control quantity in the rolling interval is obtained. Matlab is used to simulate the designed controller and compare it with the commonly used L1 guidance law, and the results of the simulation demonstrate that the NMPC has superior control leverage.

Comparison of anti-interference ability between PID controller and ADRC controller in UAV operation at ocean

With the maturity of UAV technology, drones can carry different instruments in the air to help people complete their work more efficiently. However, different working environments also bring different challenges to UAV control systems. This paper mainly discusses the quadrotor UVA and compares the stability of the Proportional Integral Derivative (PID) controller and Active disturbance rejection controller (ADRC) under the disturbance of gusts at sea. The flight principle of the quadrotor and the dynamic model of the quadrotor will be discussed on this basis. Then the composition and mathematical formula of the PID and ADRC controllers are introduced and compared. In general, this paper focused on the anti-jamming ability of different controllers under the influence of gust, which shows that although the ADRC controller has a more complex system and tedious parameter adjustment process in comparison with the PID controller, it has excellent anti-gust interference ability and can better serve the offshore operation of UAV.

Quadrotor’s modeling and control system design based on PID control

The quadrotor is an unmanned aerial vehicle with a compact construction and good maneuverability. In recent years, this type of aircraft has been widely developed and has a wide range of military and civil applications. Due to the dynamic characteristics of strongly coupled, non-linear, and multi-variable quadrotor UAVs, modeling the dynamics of UAVs and designing control systems has been the focus of quadrotor research. This paper takes the quadrotor UAV as the research object. Firstly, for the basic structure of the quadrotor UAV, the aircraft-body coordinate system and the earth-surface coordinate system of the UAV flight are established, and the transformation matrix of the two coordinate systems is given. Based on a series of reasonable assumptions, the non-linear system is modeled and a simulation platform is built using Simulink. Secondly, based on the constructed non-linear dynamics equations of the system, a cascade PID-based attitude controller and position controller of the UAV are designed. Finally, the designed control system was simulated in Matlab for straight-line flight and target-position flight, and obstacles were applied to the vehicle to verify the effectiveness of the control system. The final results show that the designed control system can make the quadrotor UAV stable in vertical and horizontal motion and can successfully travel to the target position. This paper provides a control method and framework for a PID controller-based quadrotor UAV control system.

DRL: Dynamic rebalance learning for adversarial robustness of UAV with long-tailed distribution

Adversarial robustness has attracted extensive studies in various fields by increasing the interpretability of deep learning and enhancing the understanding of neural network models. In realistic scenarios such as UAV control system, imbalanced datasets are a consensus. Therefore, how to solve the adversarial robustness on imbalanced datasets is a more and more inescapable problem in UAV control system. There have been some works on adversarial robustness on imbalanced datasets, which bring us a deeper understanding of vulnerability of deep neural networks and the generation of adversarial examples. To adjust the classification plane after training, the long-tailed robustness framework is usually designed to be multi-stage, and different classifiers are used in different stages, which can improve the robustness through multi learning. The existing methods are hardly considered to effectively handle the long-tailed robustness problem in UAV control system. To explore the intrinsic features of long-tailed robustness, we propose a one-stage robustness framework. First, we study different classifiers and propose a general cosine classifier. By changing the general cosine classifier adaptively, the model obtains a more robust classification. Then, we analyze the scalability of the focal loss and design a focal-margin loss. Finally, we design a category focus mobile learning strategy, obtained more robust features by changing the learning emphasis with this strategy. From this, we design a simple and efficient one-stage dynamic adversarial robustness method DRL under long-tailed distribution, which consists of an adaptive cosine classifier and a focal-margin loss under long-tailed mobile learning. The extended experiments demonstrate the superiority of our approach over other state-of-the-art methods, and the effectiveness of the designed module. This method can effectively solve the long-tailed robustness problem on UAV control system and other terminals.

Design of STM32-based Quadrotor UAV Control System

Design of STM32-based Quadrotor UAV Control System

ՈՒՂՂԱՁԻԳ ՎԵՐԹԻՌՈՎ ԵՎ ՎԱՅՐԷՋՔՈՎ ԱԹՍ-Ի ԿԻՆԵՄԱՏԻԿ ՎԵՐԼՈՒԾՈՒԹՅՈՒՆԸ ԵՎ ԿԱՌԱՎԱՐՄԱՆ ՀԱՄԱԿԱՐԳԻ ՆԱԽԱԳԾՈՒՄԸ MATLAB SIMULINK ՄԻՋԱՎԱՅՐՈՒՄ / KINEMATICS ANALYSIS AND CONTROL SYSTEM DESIGN OF VERTICAL TAKE-OFF AND LANDING UAV IN MATLAB SIMULINK ENVIRONMENT

The kinematic structure was designed for a new type of pitch-decoupled vertical take-off and landing UAV (VTOL UAV), based on which the equations and matrix describing its kinematics were derived. The kinematics equations were derived based on Denavit-Hartenberg’s method for robot manipulators, considering the VTOL UAV as a robotic system and its connections as kinematic connection links of the robot. A reference frame was assigned to each of the separate links, from which the transition to the coordinate system of the next link was described by a homogeneous transformation matrix. For each link, its position with respect to its own reference frame was calculated. Based on that, the kinematics matrix was derived, which allows for calculating the required torques of the UAV. In the MATLAB SIMULINK environment, an UAV control system was designed that is based on the kinematic matrix and linear, rotational motion dynamics equations. To check the system’s operability, a proportional, Integral-Derivative (PID) regulator with pre-selected coefficients was selected as a regulator. System research was carried out with pitch, roll, yaw, and decoupled pitch axes as well as height axis.

Adaptive Neural Network Extended State Observer-Based Finite-Time Convergent Sliding Mode Control for a Quad Tiltrotor UAV

The internal and external disturbance of the quad tiltrotor UAV stem from its rotor tilting motion and wind disturbance, which affect the control stability of the quad tiltrotor UAV in full flight. This paper proposes a control law design method based on the Adaptive Neural Network Extended State Observer-based Finite-Time Convergent Sliding Mode Control (ANNESO-FTCSMC). The adaptive neural network obtains the unknown total disturbance term that needs to be compensated in the finite-time convergent sliding mode control. An extended state observer is used to estimate the state variables of the controlled plant. The stability of the ANNESO-FTCSMC controller is proved with the Lyapunov stability theory. The unknown total disturbance as the expanded state variable of the state observer has the advantage of high adaptability, and the ANNESO-FTCSMC has a strong ability to compensate for the internal and external disturbances of the system. Compared with traditional sliding mode control, ANNESO-FTCSMC does not contain switching terms, so ANNESO-FTCSMC is chattering-free and converges faster. Based on this method, the flight control system of the quad tiltrotor UAV is designed and verified by hardware-in-loop simulation. The hardware-in-loop simulation results of attitude control in different flight modes and trajectory tracking control in full flight show that the ANNESO-FTCSMC controller is suitable for the flight control of quad tiltrotor UAV. Compared with the PID controller and ADRC controller, ANNESO-FTCSMC controller has more stable flight state of quad tiltrotor UAV under different flight modes and the same total disturbance. The transition process of ANNESO-FTCSMC controller from helicopter mode to airplane mode is also smoother and will not have large fluctuations. Therefore, ANNESO-FTCSMC controller has strong anti-disturbance and robustness.

Improving the accuracy of the UAV following along a given trajectory under wind loads by the SPSA method

Abstract One of the main areas of UAV application is the collection of data with their exact binding to the selected coordinate system (for example, aero photography). In this case, it is important to not only get the exact coordinates of data collection, but also to ensure the minimum deviation of the UAV from the path under the conditions of external disturbances (wind loads) acting on it. In the article a procedure for assessing wind speed and direction using the SPSA method is proposed. The results of simulation modeling of the algorithm's operation confirmed during field tests on an ultralight UAV are presented. Key words UAV control system, randomized algorithms, Kalman filter, GNSS, random process prediction methods. Acknowledgements This research is supported by St. Petersburg State University, project no. 73555239.

An investigation of the Control Quality of the Automatic Control System for Fixed-wing UAVs During Landing Process

This study presents an investigation and evaluation of the control quality of the automatic control system for UAVs in the vertical plane under windy conditions. For the operational stages of UAVs in general, the landing stage is one of the high-probability stages that pose a threat to flight safety, especially at the time of landing. Therefore, to evaluate the control quality of the system, the authors investigated the parameters during UAV landing. The automatic control system uses a PID controller with optimal parameters selected by the Signal Constraint tool in Matlab Simulink. The predetermined wind model was used to verify at the most extreme times. The programs proposed in the paper are simulated on Matlab Simulink software.

Research on Attitude Control System of Quadrotor Unmanned Aerial Vehicle based on Fuzzy PID Control Algorithm

The control of quadrotor aircraft mainly includes attitude control and position control. Because the change of position is caused by the change of attitude, it is most important to control the attitude of quadrotor aircraft. PID control principle is simple, easy to master, convenient to use, strong adaptability and good robustness, and its control quality is insensitive to the change of the controlled object, so it is most widely used. Quadrotor UAV is a strongly coupled underactuated system, and its stability is easily affected by system uncertainty and external interference during flight. Therefore, an improved fuzzy PID control method is proposed. In this article, the significance and ideas of constructing the attitude control system of quadrotor UAV based on fuzzy PID control algorithm are analyzed. This control strategy not only keeps the advantages of fuzzy control algorithm, which is independent of accurate model, and flexible and fast control, but also combines the advantages of PID control algorithm with small static error, and can achieve good control of nonlinear complex system.

Design of INS/GNSS Precise Posture Measurement and Positioning System for Complex System

In this paper, aiming at the robustness of the UAV control system and the accuracy of positioning and mapping in complex situations, the visual odometry is used as the front end of the positioning and mapping system, and the data of the IMU inertial sensor is fused on the basis of the monocular camera. The analysis completes the loosely coupled initialization process; the back-end constructs the cost function to realize the tightly coupled optimization, and uses the loop closure detection relocation and the four degree of freedom graph optimization to realize the nonlinear optimization. It is verified by simulation that the algorithm can improve the visual robustness in time when the UAV moves quickly or the scene has wrong results, so that the trajectory captured by the camera and the real-time construction map maintain global consistency, effectively eliminating the integral drift of the IMU, and correcting The noise of the IMU improves its positioning accuracy.

RESEARCH OF THE INERTIAL DEVIATION MEASUREMENT SYSTEM UAV CARGO FROM ZERO POINT

Inertial measurement units (IMUs) can consist of one or more sensors that collect data designed to measure inertial motions in a specific frame of reference. Acceleration, rotational speed are examples of data that can be obtained from the sensors contained in the IMU. IMUs can be found in a variety of smart devices, medical rehabilitation, general robotics, UAV control and navigation enhancement systems, sports devices, and virtual reality systems. Some engineering solutions and displacement sensors have a number of limitations, such as systems that do not have a clear initial reference point. In such systems, it is difficult to track deviations from the starting point. However, the acceleration of a moving object is usually easy to measure, so the given deflection of the object can easily be obtained using the integral of the acceleration. To solve such problems, a distance measurement system based on a microelectromechanical system (MEMS) accelerometer has been developed. The hardware structure of the system includes a data measurement module and integrated data collection and data processing modules. Due to the physical properties of the sensor, such a system can be used in a small space, with large overloads and other adverse conditions. This article describes a system calibration method and proposes a deviation tracking algorithm based on acceleration integration. Fixed output error caused by sensor mounting position is analyzed. The analysis shows that if this factor is not taken into account, the error will lead to a serious error. The experiment of measurement of the deviation from the zero point is carried out on a horizontal, flat surface, and the calculation results show that the average accuracy of the deviation measurement of this system can reach 99.05%. The results of the experiment indicate the feasibility of the system in the conditions of short-term free fall and the validity of the data processing algorithm. The accuracy of the distance measurement system can meet most engineering needs.