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Abstract Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel–Ziv Complexity (LZC), and Teager–Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations. The extracted features were subjected to a Chi-Square (χ2) feature selection process to identify the most significant features for input into a Deep Neural Network. The Taguchi method was utilized to test the performance of the recorded features, correspondingly. Performance metrics, including Accuracy, F1-Score, Precision, and Recall, were employed to evaluate the model’s effectiveness before and after the feature selection. The achieved accuracy has increased by 0.9% when compared with results utilizing traditional statistical methods. Comparative analysis with prior research reveals that the proposed untraditional features surpass traditional methods in diagnosing UAV propeller faults. It resulted in improved performance metrics with Accuracy, F1-Score, Precision, and Recall reaching 99.6%, 99.5%, 99.5%, and 99.5%, respectively. The results suggest promising directions for future research in UAV maintenance and safety protocols.

This study introduces a novel methodology for enhancing the efficiency of solar-powered unmanned aerial vehicles (UAVs) through azimuthal solar synchronization and aerodynamic neuro-optimization, leveraging the principles of slime mold neural networks. The objective is to broaden the operational capabilities of solar UAVs, enabling them to perform over extended ranges and in varied weather conditions. Our approach integrates a computational model of slime mold networks with a simulation environment to optimize both the solar energy collection and the aerodynamic performance of UAVs. Specifically, we focus on improving the UAVs’ aerodynamic efficiency in flight, aligning it with energy optimization strategies to ensure sustained operation. The findings demonstrated significant improvements in the UAVs’ range and weather resilience, thereby enhancing their utility for a variety of missions, including environmental monitoring and search and rescue operations. These advancements underscore the potential of integrating biomimicry and neural-network-based optimization in expanding the functional scope of solar UAVs.

The performance of quadcopter frames, particularly in terms of weight and crash resistance, is significantly influenced by their structural design and manufacturing process. In this work, a methodology is proposed that integrates advanced principles of topology optimization (TO) and additive manufacturing (AM) techniques to optimize the frame structure for improved performance. First, an analysis is conducted to evaluate existing quadcopter frame configurations, identifying areas for improvement. Experimental evaluations of thrust and moment of motors are performed to assess the performance of the enhanced quadcopter frame, with a focus on advancing the design through computer-aided simulations of static structural analysis and impact tests. The TO technique is then employed to determine the optimal distribution of material within the frame, governed by constraints such as weight reduction and mechanical strength. The results demonstrate that the overall performance of a quadcopter frame is significantly improved by the proposed methodology, showcasing advancements in stability, weight reduction, and crashworthiness. The resulting optimized frame design is subsequently manufactured using AM methods, which offer advantages such as design flexibility and the ability to produce complex geometries. The findings of this study contribute to the field of quadcopter design and optimization by highlighting the synergies between TO and AM techniques. An avenue is offered for the development of lightweight and robust quadcopter frames, as the capabilities and performance of quadcopter systems are advanced. The insights gained from this research open up opportunities for further advancements in the design and manufacturing of UAVs.

This work applies an adaptive PD controller based on MRAS (Model Reference Adaptive System) using Lyapunov theory with sliding mode theory to a Fixed-wing MAV (Mini Aerial Vehicle). The objective is to design different adjustment mechanisms to obtain a robust adaptive control law in the presence of unknown perturbation due to wind gusts. Four adjustment mechanisms applied to an adaptive PD controller are compared. The adjustment mechanisms are Lyapunov theory, Lyapunov theory with first-order sliding mode, Lyapunov theory with second-order sliding mode, and Lyapunov theory with high-order sliding mode. Finally, after several simulations, a significant reduction and almost elimination of the unknown perturbations are presented with the addition of the sliding mode theory in the design of the adjustment mechanism for the adaptive PD controller.

Over the past few years, there has been an increasing fascination with electric unmanned aerial vehicles (UAVs) because of their capacity to undertake demanding and perilous missions while also delivering advantages in terms of flexibility, safety, and expenses. These UAVs are revolutionizing various public services, encompassing real-time surveillance, search and rescue operations, wildlife assessments, delivery services, wireless connectivity, and precise farming. To enhance their efficiency and duration, UAVs typically employ a hybrid power system. This system integrates diverse energy sources, such as fuel cells, batteries, solar cells, and supercapacitors. The selection of an appropriate hybrid power arrangement and the implementation of an effective energy management system are crucial for the successful functioning of advanced UAVs. This article specifically concentrates on UAV platforms powered by batteries, incorporating innovative technologies, like in-flight recharging via laser beams and tethering. It provides an all-encompassing and evaluative examination of the current cutting-edge power supply configurations, with the objective of identifying deficiencies, presenting perspectives, and offering recommendations for future consideration in this domain.

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of sensor usage and the system’s working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.

The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table.

There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.

In the paper, a new cascade control system for an autonomous flight of an unmanned aerial vehicle (UAV) based on Proportional–Integral–Derivative (PID) and finite-time State-Dependent Riccati Equation (SDRE) control is proposed. The PID and SDRE controllers are used in a hybrid control system for precise control and stabilization, which is necessary to increase the drone’s flight stability and maneuver precision. The hybrid PID-SDRE control system proposed for the quadrotor model is quasi-optimal, since the suboptimal control algorithm for the UAV stabilization is used. The combination of the advantages of PID and SDRE control gives a significant improvement in the quality of control while maintaining the simplicity of the control system. Furthermore, the use of the suboptimal control technique provides the UAV attitude tracking in finite time. These remarks are drawn from a series of simulation tests conducted for the drone model.

With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault’s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model’s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well.

In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.

Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface moving targets and visual images, this study proposed and designed a sea-surface moving-target visual detection and tracking system with a multi-camera cooperation approach. In the system, the technologies of moving target detection, tracking, and matching are studied, and the strategy to coordinate multi-camera cooperation is proposed. The comprehensive experiments of cooperative sea-surface moving-target visual tracking show that the method used in this study has improved performance compared with contrapositive methods, and the proposed system can meet the needs of multi-camera cooperative visual tracking of moving targets on the sea surface.

This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).

In this paper, an easily implementable coaxial quadrotor model and its validation on data from a real unmanned aerial vehicle (UAV), are presented. The proposed mathematical model consists of two parts: description of orientation and position of the UAV in the three-dimensional space. It takes into consideration the gyroscopic effect, influence of the Coriolis force, viscous friction and a several drag-like effects (blade flapping, rotor drag, translational drag and profile drag). In contrast to multirotor models available in the literature, this one is characterized by complementarity in relation to the available control techniques. Depending on selection of these techniques, the model can be narrowed (simplified) to meet the needs without the loss of behaviour adequacy to a real UAV.

The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach.

In this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump system). The main goal of the paper is to propose an object that potentially can have similar to the power system grid properties, but with the number of state variables reduced twice (only one state variable for each node, while there are two in the case of power systems). Transition and measurement functions are proposed in the paper, and two types of transition functions are considered: dependent on one or many state variables. In addition, 10 types of measurements are proposed both for branch and nodal cases. The experiments are performed for 14 different, four-dimensional systems. Plants are both linear and highly nonlinear. The results include information about the state estimation quality (based on the mean squared error indicator) and the values of the effective sample size. It is observed how the higher effective sample size resulted in the better estimation quality in subsequent cases. It is also concluded that the very low number of significant particles is the main problem in particle filtering of multivariable systems, and this should be countered. A few potential solutions for the latter are also presented.

With an increasing number of multirotor unmanned aerial vehicles (UAVs), solutions supporting the improvement in their precision of operation and safety of autonomous flights are gaining importance. They are particularly crucial in transportation tasks, where control systems are required to provide a stable and controllable flight in various environmental conditions, especially after changing the total mass of the UAV (by adding extra load). In the paper, the problem of using only available basic sensory information for fast, locally best, iterative real-time auto-tuning of parameters of fixed-gain altitude controllers is considered. The machine learning method proposed for this purpose is based on a modified zero-order optimization algorithm (golden-search algorithm) and bootstrapping technique. It has been validated in numerous simulations and real-world experiments in terms of its effectiveness in such aspects as: the impact of environmental disturbances (wind gusts); flight with change in mass; and change of sensory information sources in the auto-tuning procedure. The main advantage of the proposed method is that for the trajectory primitives repeatedly followed by an UAV (for programmed controller gains), the method effectively minimizes the selected performance index (cost function). Such a performance index might, e.g., express indirect requirements about tracking quality and energy expenditure. In the paper, a comprehensive description of the method, as well as a wide discussion of the results obtained from experiments conducted in the AeroLab for a low-cost UAV (Bebop 2), are included. The results have confirmed high efficiency of the method at the expected, low computational complexity.

The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.

The list of photovoltaic (PV) applications grows longer every day with high consideration for system efficiency. For instance, in spite of many recent PV aircraft designs, aircraft propulsion was mainly reserved for nonelectric motors. Lately, the Solar Impulse flight across the world shows the possibilities of larger PV powered electric aircraft. In order to obtain this goal efficiency of flight, PV conversion, power converters and electric drives have to be maximized. These demands led to a 63.4 m wingspan. The purpose of this paper is to present that PV power could be used for improving the performance of fixed-wing radio-controlled aircrafts with smaller wingspans (1 m). In order to improve the performance of battery powered electric unmanned aerial vehicles (UAV), a model without PV cells (commercial Li-ion battery powered UAV) was compared with UAV powered both from battery and PV modules. This work shows details about Boost DC/DC converter and PV system design for small size fixed-wing electric UAVs, investigating the possibility of the application of PV powered drones, as well. Theoretical findings involving efficiency improvements have been confirmed by measurements combining the improvements in electrical engineering, microcontroller application and aerodynamics.