Unmanned Aerial Vehicle Control System Research Articles

Monitoring the State of the Operator of the Ergatic System in UAV Control Tasks

An important aspect of the application of unmanned systems is the reliability and safety of controlling these devices. An innovative approach has been proposed to improve the efficiency of the pilot operator and the reliability of the automated control system. It includes the development of an algorithm for determining the pilot’s condition based on heart rate analysis. This algorithm helps to assess the condition of the pilot and his ability to control the drone. Another important element of the proposed approach is the algorithm for selecting the control mode of the automated control system for unmanned aerial vehicles, which takes into account information about the functional state of the pilot operator. This algorithm allows the system to automatically switch between different operating modes depending on the condition of the pilot, ensuring optimal control and minimizing the risks of human error. An integrated approach to improving the reliability of the unmanned aerial vehicle control system allows not only improving the work of the pilot operator, but also ensuring the safer and more efficient operation of automated systems.

Distributed Localization for UAV–UGV Cooperative Systems Using Information Consensus Filter

In the evolving landscape of autonomous systems, the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has emerged as a promising solution for improving the localization accuracy and operational efficiency for diverse applications. This study introduces an Information Consensus Filter (ICF)-based decentralized control system for UAVs, incorporating the Control Barrier Function–Control Lyapunov Function (CBF–CLF) strategy aimed at enhancing operational safety and efficiency. At the core of our approach lies an ICF-based decentralized control algorithm that allows UAVs to autonomously adjust their flight controls in real time based on inter-UAV communication. This facilitates cohesive movement operation, significantly improving the system resilience and adaptability. Meanwhile, the UAV is equipped with a visual recognition system designed for tracking and locating the UGV. According to the experiments proposed in the paper, the precision of this visual recognition system correlates significantly with the operational distance. The proposed CBF–CLF strategy dynamically adjusts the control inputs to maintain safe distances between the UAV and UGV, thereby enhancing the accuracy of the visual system. The effectiveness and robustness of the proposed system are demonstrated through extensive simulations and experiments, highlighting its potential for widespread application in UAV operational domains.

VIDEO REPEATER DESIGN CONCEPT FOR UAV CONTROL

 This study is aimed at the utilization of a video repeater for unmanned aerial vehicle (UAV) control, involving a comparative analysis of existing scientific literature, methodologies, and available solutions. Through comparative analysis, the method of using an external repeater emerged as the most promising, offering flexibility in selecting repeaters with superior performance and advanced capabilities. Notably, this approach allows for the utilization of a single repeater across multiple UAVs and facilitates convenient modification or upgrade of the repeater without necessitating alterations to the UAV itself. Following component selection, an experimental prototype was designed to facilitate empirical investigations. Additionally, a frequency transmission scheme was devised for quadcopter control employing the repeater. This research contributes to advancing UAV control systems by introducing a novel approach to video repeater integration, poised to enhance operational efficiency and adaptability in diverse settings. The findings from this study are anticipated to inform further developments in UAV technology, particularly in optimizing video transmission for improved situational awareness and mission effectiveness.

Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance

ObjectiveThe difficulty of unmanned aerial vehicle (UAV) control recently lies in multidirectional movement in 3-dimensional space, improving control accuracy and manipulation safety. To address these challenges, a UAV control system that incorporates a hybrid brain-computer interface (hBCI), gyroscope and visual obstacle avoidance based on monocular depth estimation is proposed. Approach. We propose an efficient steady-state visual evoked potential (SSVEP) classification network (CL-NET) featuring a one-dimensional convolutional neural network, a long short-term memory module and an attention module to identify the user's intention for UAV movement in the front, back, left and right directions. The take-off, landing and rising control of the UAV is realized by an electrooculogram (EOG) signal detection algorithm, a blink state detector. In addition, the UAV can fly in an oblique state and rotate according to the current head posture detected by a gyroscope. Furthermore, an improved monocular depth estimation network is employed to design the autonomous obstacle avoidance module of the UAV, ensuring the safety of the brain-controlled system in practice. Main results. The proposed CL-NET delivers an accuracy of 98.67% on the public dataset and an accuracy of 97.92% on the self-collected dataset, both of which surpass the performance of state-of-the-art models. Additionally, we set up a brain control group and a remote control group to conduct practical experiments in a realistic environment. In the experiments involving sixteen subjects, the proposed UAV control system reached an average information transfer rate (ITR) of 44.09 bits/min, and the brain control group had a lower collision rate than the remote control group. Significance. The hybrid control method ensures that the multi-degree-of-freedom (multi-DOF) UAV control system maintains outstanding performance while ensuring good safety.

Artificial Intelligence Methods in Automated Unmanned Aerial Vehicles Control Systems

One of the main problems in ensuring the unmanned aerial systems (UAS) safety and control performance indicators is the operational analysis organization of heterogeneous data coming from on-board sensors and the formation of adequate recommendations and decisions on their basis of flight missions implementation. In recent years, there have been many research papers devoted to solving this problem using artificial intelligence (AI) methods. The article discusses AI methods for using in tasks related to UAS. We have described the sources of information to generate the data necessary for the application of AI methods. We have classified typical tasks of computer vision and navigation systems for solving using AI methods. We have analyzed the generally accepted classification of AI methods within the scope of the research subject. At the same time, special attention is paid to the features of AI methods that allow solving many well-known problems of recognition, approximation, optimization for UAS target and navigation tasks realization and effective operator support. In particular, we have considered neural networks, decision trees, support vector machines, k-nearest neighbors, genetic, ant colony algorithms, artificial immune systems. Currently, the hardware allows integrating complex algorithms based on these methods on board and widely using them in flight missions. The results of the study conducted as part of the review are illustrated by examples from the scientific publications.

An improved seeds scheme in K‐means clustering algorithm for the UAVs control system application

AbstractClustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

Identification Method of Unmanned Aerial Vehicle Graphical Control Strategy Based on Cloud Server

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in agricultural plant protection, electric power inspection, security patrols, and other fields. However, the control system of the UAV is a complex human–computer interaction system, which requires higher requirements in practical applications. Due to differences in hardware design, software development, and other aspects among different manufacturers, these UAV control systems require high hardware requirements, resulting in a long development cycle. At the same time, in practical applications, due to various reasons, there are equipment failures that are difficult to detect and eliminate in a timely manner. This paper used the UAV graphical control strategy identification method based on cloud server technology, and used the support vector machine (SVM) algorithm to analyze its identification accuracy. The research results showed that when other conditions were the same, the number of researchers and experts who were satisfied with the drone trial effect of the cloud server was 42 and 10, respectively, accounting for 84% and 100%. It indicates that they believe that the cloud server can effectively improve the effectiveness of the drone graphical control strategy recognition method, indicating a positive relationship between the two.

A Bio-Inspired Safety Control System for UAVs in Confined Environment With Disturbance.

This article presents a bio-inspired safety control scheme for unmanned aerial vehicles (UAVs) in confined environments with disturbance. Although there has been some existing research on the effect of disturbance for a single UAV, multi-UAV formation under external wind disturbances remains challenging, especially in a tight and confined environment. Inspired by nature, this study concentrates on an anti-disturbance mechanism for safe multi-UAV formation in a tight environment. The presented safety control system combines disturbance observer-based control (DOBC), bionic formation switching (BFS) strategy, and safety evaluation. Two safety issues are considered in this article. For a single UAV, the estimated disturbance is compensated in the inner-loop controller. While for multi-UAV formation, the BFS strategy attenuates the effect of external wind disturbance leveraging the formation configuration. The so-called group perturbation immune factor (GPIF) is designed to analyze and evaluate the safety of the overall formation. The experimental results validate the comprehensiveness and anti-disturbance capability of the system.

Simultaneous attitude fault detection and control of the six-rotor UAV based on event trigger mechanism

This paper analyzes the problem of simultaneous fault detection and control of the six-rotor unmanned aerial vehicle control system, considering external disturbance, measurement disturbance, and actuator fault. The integral event trigger mechanism is introduced in the control side and sensor side of the system. Based on Lyapunov stability and H∞ control theory, the sufficient conditions to make the fault system asymptotically stable and have certain performance indexes are given by means of linear matrix inequalities; at the same time, the design criteria of event trigger parameters are also given. The linear matrix inequality is decoupled, and the calculation method of gain matrix of simultaneous fault detection and control module is given. The effectiveness of the proposed method is verified by simulation experiments.

Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision

Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and velocity measurements, while vision sensors capture fine-grained visual information. By integrating data from these modalities, the system achieves a comprehensive understanding of hand gestures, resulting in improved gesture recognition accuracy and robustness. The proposed system comprises three main stages: data acquisition, gesture recognition, and multimodal fusion. In the data acquisition stage, synchronized data streams from FMCW radar and vision sensors are captured. Then, machine learning algorithms are employed in the gesture recognition stage to classify and interpret hand gestures. Finally, the multimodal fusion stage aligns and fuses the data, creating a unified representation that captures the spatial and temporal aspects of hand gestures, enabling real-time control commands for the UAV. Experimental results demonstrate the system‘s effectiveness in accurately recognizing and responding to hand gestures. The multimodal fusion of FMCW radar and vision sensors enables a robust and versatile gesture-based control interface.

Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm

This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs.

A flight control system for quadrotor plant protection UAV based on RBF-PID algorithm

As a powerful tool for modern agricultural protection, unmanned aerial vehicle (UAV) has the advantages of good adaptability and high efficiency in spraying and other operations compared with traditional manual operations The attitude and control of plant protection UAV are greatly affected by external factors during operation. However, the traditional proportional-integral-derivative (PID) controller commonly used at present cannot adapt to the complex environment because of the fixed parametersTherefore, a more sensitive method is needed to achieve adaptive balance of the aircraft attitude. In this paper, we establish the dynamic mathematical model of quadrotor UAV, and the influence of air resistance and rotational torque is considered in the process. Adopting the combined control method of Radial Basis Function (RBF) neural network and PID, the dynamic tuning of system control parameters is achieved through the self-learning of neural network and the feature of nonlinear mapping. The control system not only has the advantages of simple PID control structure and clear physical meaning, but also has the ability of neural network self-learning and adaptation. Matlab/Simulink was used as the experimental platform to respectively simulate the RBF-PID control system and the traditional PID control system. The simulation results show that the control system based on RBF-PID algorithm has a shorter response time and stronger robustness, which enhances the systems self-adaptability

Development of an Automatic Solar Tracker Control System for a Tandem-Winged UAV and Its Implementation Strategies

Solar power is a popular option for powering Unmanned Aerial Vehicles (UAVs) due to its ability to provide power for long-endurance flight. However, solar-powered UAVs face challenges, including operational reliability problems. To address their operational reliability issues, this paper proposes an automatic solar tracker system and tests two implementation strategies for controlling a tandem-winged and solar-powered UAV, developed by the Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung. Due to its unconventional configuration, a mathematical model for the UAV is developed to evaluate the vehicle’s dynamic characteristics. Based on these, three feedback control systems are designed, i.e., roll attitude control, a heading tracker, and a solar tracker. Two implementation strategies are proposed to combine the control systems, i.e., the mode-switching and the simultaneous tracking strategies. A series of simulations are then conducted to check the vehicle’s overall flight performance, as well as the gathering of solar energy. The mode-switching strategy was able to gather up to 13% more solar energy than the simultaneous strategy, which could only reach slightly above 2%. Mode-switching, however, resulted in a shorter range compared to the latter, due to the time spent in the charging mode, flying in a circular pattern.

Energy Minimization for UAV Swarm-Enabled Wireless Inland Ship MEC Network With Time Windows

This paper establishes a new unmanned aerial vehicle (UAV) swarm-enabled wireless inland ship mobile edge computing (MEC) network with time windows, where UAVs are deployed to support time-constrained and low-resource unmanned surface vehicles (USVs) communication and computation. We aim to minimize UAV swarm energy consumption subject to numerous constraints. To tackle the challenging formulated problem, we decompose the original challenging problem into a list of sub-problems according to block coordinate descent (BCD) method and propose a heuristic algorithm to solve subproblems in an iterative manner. Specifically, the optimization of USVs offloading decisions is formulated as mixed-integer non-linear programming and then a modified deferred acceptance algorithm is proposed. The joint consideration of UAVs flight speed and UAV swarm stability is converted into a single-objective optimization problem after determining UAV control system’s latency requirements, and then Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) conditions is proposed. Successive convex approximations (SCA)-based algorithm is proposed to convert the non-convex constraints to convex ones, and then UAVs hovering coordinates can be obtained by solving the convex approximation problem. Numerical results verify the effectiveness of jointly considering USVs offloading decisions, UAVs flight speed, UAVs horizontal hovering coordinates and the number of UAVs for energy efficiency in comparison with several advanced baseline algorithms without optimization design.

Forced Quadcopter Control

The problem of analytical synthesis of the control acceleration of an unmanned aerial vehicle (UAV) of a multirotor type is being solved in relation to a light quadrocopter with a high flight duration. The optimal control is analytically determined for a given minimized quality functional in the form of the minimum time required to transfer the UAV from a given initial to a given final position in space. A mathematical model of the movement of the UAV mass center in a given plane relative to the earth's surface is considered. A feature of the proposed technique is the solution of the problem of maximum speed (forced control) based on the consideration of the laws of kinematics of uniformly accelerated motion of a rigid body. For given characteristics of the maximum allowable speed and control acceleration of the UAV, the moments of switching of the control signal are analytically calculated, which can be implemented in the UAV autopilot. This allows, in contrast to classical methods for solving the problem of forced control, to get rid of the need to solve a two-point boundary value problem and consider additional transversality conditions. The computer simulation of the obtained analytical results in the form of processes of changing the control acceleration, as well as the UAV motion parameters, has shown the efficiency of the proposed technique and the prospects for its use at the initial stage of the synthesis of the UAV control system.

Simultaneous UAV having actively sweep angle morphing wing and flight control system design

PurposeThe purpose of this paper is to improve autonomous flight performance of an unmanned aerial vehicle (UAV) having actively sweep angle morphing wing using simultaneous UAV and flight control system (FCS) design.Design/methodology/approachAn UAV is remanufactured in the ISTE Unmanned Aerial Vehicle Laboratory. Its wing sweep angle can vary actively during flight. FCS parameters and wing sweep angle are simultaneously designed to optimize autonomous flight performance index using a stochastic optimization method called as simultaneous perturbation stochastic approximation (SPSA). Results obtained are applied for flight simulations.FindingsUsing simultaneous design process of an UAV having actively sweep angle morphing wing and FCS design, autonomous flight performance index is maximized.Research limitations/implicationsAuthorization of Directorate General of Civil Aviation in Turkey is crucial for real-time UAV flights.Practical implicationsSimultaneous UAV having actively sweep angle morphing wing and FCS design process is so beneficial for recovering UAV autonomous flight performance index.Social implicationsSimultaneous UAV having actively sweep angle morphing wing and FCS design process achieves confidence, high autonomous performance index and simple service demands of UAV operators.Originality/valueComposing a novel approach to improve autonomous flight performance index (e.g. less settling and rise time, less overshoot meanwhile trajectory tracking) of an UAV and creating an original procedure carrying out simultaneous UAV having actively sweep angle morphing wing and FCS design idea.

Features of simulation of operator control of unmanned aerial vehicle and its target load

The article is devoted to the formulation of the problem of synthesis of the control system of an unmanned aerial vehicle (UAV) with the participation of a human operator. An analysis is made of various approaches to the synthesis of systems, models of a human operator as a link in a tracking system, mathematical models and information support for complex human-machine systems, competencies required by UAV operators. The system considers a human operator as a dynamic link that performs compensatory monitoring of the relevant information that is presented on the monitor of the corresponding operator controlling the UAV or its target load in accordance with the task. On the basis of the analysis, a generalized scheme of the UAV control system and its target load is substantiated, a mathematical model of a human operator is studied in the form of a transfer function consisting of five elementary links that take into account the dynamic characteristics of the central nervous system, the delay of the neuromuscular system, the ability of the human operator to smooth random fluctuations of the information presented on the monitor screen received from the telemetry system. As a typical example of a human-machine control system, the ground control point of the multifunctional unmanned aerial complex “Busel” is considered, which includes two workplaces for the UAV control operator and the target load control operator, which is considered equipment designed to monitor objects located on the earth’s surface

Study of permutation decoding algorithms in UAV control systems

Relevance. This paper presents relevant topic of the use of unmanned aerial vehicles (UAV) in the agroindustrial complex (AIC) to obtain timely reliable information about the state of agricultural land for the entire period. In modern infocommunication technologies and communication systems, high requirements are placed on the reliability of information transmission. In mobile data exchange systems, compositions of noise-resistant codes are used in the form of cascade designs or turbo codes, and in order to increase their efficiency, it is necessary to make the fullest use of the redundancy introduced at each stage of data processing. To solve such a problem, it is advisable to apply a decoding method based on the ordering of the character confidence indices (CCI) of the code combination.Methods. When determining the encoding-decoding systems, the Mathcad application software was used. Mathematical studies have shown that binary block codes for reaching the specified boundary can be decoded not only on the basis of ordered statistics, but also using the method of dividing the space of allowed code combinations into clusters. Based on this method, a block diagram of the algorithm of the simulation model of the operation of the block code decoder based on cluster calculation is compiled.Results. The application of the proposed method of soft decoding of systematic block codes reduces the processing time of code combinations in the decoder by reducing the dimension of the square matrix to determine the nonlinearity property of the rows of the rearranged generating matrix after ordering the columns of the original generating matrix.

Problems of AES encryption without hardware acceleration on BROADCOM BCM2711chips

In today’s world, information security plays a critical role in various aspects of our lives. Over the past decade, data encryption has become the primary means of ensuring confidentiality, integrity, and availability of information. Using modern encryption algorithms, we can protect our data from unauthorized access and malicious use. With the development of technology, single-board computers are becoming more popular and functional. The use of single-board computers can significantly increase the functionality of UAVs. For example, using a single-board computer with real-time video processing capabilities can enable UAVs to perform tasks such as monitoring territories, tracking objects, or even performing search and rescue operations. However, devices such as unmanned aerial vehicles (UAVs), which require high data processing speeds and low energy consumption, are often limited by the lack of hardware acceleration for encryption algorithms such as AES [1]. This leads to a sharp decrease in encryption and decryption speeds. Such a limitation requires us to search for effective alternatives to the AES algorithm for such devices. Considering these limitations and the need for alternatives, the purpose of this article is to compare the performance of different encryption algorithms on the Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC [2]. We analyze the encryption and decryption speeds for AES-256, AES-512, ChaCha12, and ChaCha20 algorithms. This will allow us to understand which algorithm is most effective for use in UAV control systems and other devices built on Bcm2711 that do not have AES hardware acceleration. I hope that this analysis will help developers and engineers choose the optimal encryption algorithm for their systems, considering factors such as speed, security, and hardware limitations.

АЛГОРИТМ ОБРАБОТКИ ПРОСТРАНСТВЕННО-ВРЕМЕННЫХ СИГНАЛОВ НА ОСНОВЕ СИСТЕМЫ MIMO-OFDM В УСЛОВИЯХ АКТИВНЫХ ПОМЕХ

An algorithm for processing space-time signals based on a MIMO-OFDM communicationsystem in the presence of active interference is described. Theoretical calculations are given, thedetection of approaches that often cause the bandwidth of the communication channel when leavinga point source of active interference. The task is relevant in the conditions of dense urban development,as well as the steadily growing need to improve noise immunity and communicationquality without bandwidth coverage. To process information in a MIMO-OFDM system withoutinterference, engineers mainly use the criterion of maximum signal-to-noise ratios, however, in thepresence of active interference, the Wiener criteria. It measures the minimum RMS pilot measurementerror in OFDM. In this case, a signal receipt of a monetary amount, an eigenvector is detected,weight processing is performed, a signal is received that occurs according to the minimumerror criterion. An orthogonal frequency division multiplexing technology with pilot subcarriers isreduced at the receive frequency in frequency and modulation code. Thresholding was found in thedecrypted code, produced by the RMS comparison. The arrival of big money comes from this ownnumber. Pilot-based enumeration searches for the minimum acceptable error. The eigenvector ofthe found signal will be for weight processing. Experiments are carried out to detect a signal in acommunication channel with and without adaptation using the Wiener criterion and the presenceof interference with two types of modulation (QAM-4, BPSK). The results show that the adaptationimproves the BER (bit error rate) state along the entire line of the curve and for all SNRs.The developed algorithm can be used for communication and control systems of unmanned aerialvehicles in the presence of active interference.