Model Of Unmanned Aerial Vehicle Research Articles

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle (UAV) in the presence of parameter uncertainties, unmodeled uncertainties and wind disturbances. First, a fixed-time disturbance observer (FXDO) based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model. Then, a fixed-time integral sliding mode control (FXISMC) is combined with the FXDO to achieve strong robustness and chattering reduction. Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability. Finally, numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances. In addition, the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Improving flight performance of UAVs by ice shape modulation

Aircraft icing poses a great threat to flight safety. In response to the characteristics of high-power consumption, large volume, and heavy weight of traditional anti-/de-icing technologies, the concept of ice shape modulation is proposed, which is called ice tolerant flight. Firstly, the flight performance of Unmanned Aerial Vehicle (UAV) was compared in three states: no ice, full ice, and modulated ice through flight tests. It was found that ice shape modulation has a significant improvement effect on the aerodynamic performance of aircraft under icing conditions. Under the three modulated ice shape conditions in this experiment, the lift coefficient of the UAV under different ice shape modulation conditions increased by 18%–33%, and the stalling angle was delayed by 3°-5°. Subsequently, the pressure distribution, streamlines in the flow field, and detached vortex distribution of the UAV model in these three states were obtained through numerical simulation, to study the mechanism of ice shape modulation on the aerodynamic performance of aircraft. The simulation found that the reason for the improvement of the wings effect after ice shape modulation is that the modulated area forms a leading-edge protrusion structure similar to a vortex generator. This structure prolongs the mixed flow region on the wings surface and reduces the trend of flow separation, which plays a role in increasing lift and reducing drag for UAVs under icing conditions. Finally, a reverse reachable set that can be used for unexpected state recovery is used as the definition of flight safety boundaries, and an aircraft dynamics model is established to obtain flight safety boundaries for different states. Research has found that the flight safety boundary of the UAV in a no ice state is greater than that in a modulated ice state, and the safety boundary in a modulated ice state is greater than that in a full ice state. Compared with the full ice state, the flight safety boundary after modulation has expanded by 27.0%. The scheme of ice shape modulation can provide a basis for the flight safety of aircraft under icing conditions.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

Three-dimensional modeling and application based on UAV tilt photogrammetry technology

Amid the swift progression of digital and smart cities, the growing prominence of three-dimensional information, especially in architecture, stands out. Architecture, forming the urban area's crux, necessitates comprehensive and precise data acquisition and effective utilization for modeling. This aligns with the burgeoning emphasis on high-quality urban informatization. The advent of unmanned aerial vehicle (UAV) tilt photogrammetry technology serves as a burgeoning auxiliary method in this domain. This innovative approach enhances model accuracy through multi-angle sampling, presenting a substantial advancement in three-dimensional modeling and its applications. This document outlines a fundamental framework for three-dimensional modeling and provides an exhaustive explanation of each component. An analysis of existing deficiencies, accompanied by feasible and validated solutions, forms a crucial part of the discussion, addressing gaps and offering remedial measures for effective three-dimensional modeling. The paper further delves into the extensive applications of UAVs modeling, underscoring its significant impact in areas such as hazardous rock detection and rural development among other sectors. It highlights the pivotal role of UAV tilt photogrammetry in driving advancements in diverse fields, bolstering efficiency, accuracy, and reliability in three-dimensional modeling and its multifaceted applications, thereby contributing to the enhancement of urban informatization and the broader development agenda.

Optimization of high-speed fixed-wing UAV penetration strategy based on deep reinforcement learning

The penetration decision of unmanned aerial vehicles (UAVs) is one of the key components for UAVs to detect or strike crucial targets in adversarial environments. Presently, most methods lack research on penetration decisions under unknown interception information. The executing UAV for decision-making is generally a simplified three-degree-of-freedom model. The decision format is often simplified to path point optimization, lacking control over the stability of complex model attitudes and exhibiting relatively poor robustness. This paper proposes a penetration relative motion theory and aircraft control method based on the six-degrees-of-freedom UAV model to reduce response errors between algorithm control decisions and actual flight control. Using a Markov decision process, a UAV penetration decision control method based on an attitude-overload control loop is designed. This method combines reinforcement learning techniques to enable autonomous penetration decision-making and control for UAVs facing active defense scenarios, enhancing the autonomy, accuracy, and generalization of UAV online decision-making. The paper concludes by implementing autonomous decision-making and control for UAV penetration through the integration of interceptor trajectory prediction with proximal policy optimization. This method reduces the dependency of penetration strategies on interceptor information, ensuring both the generalization ability of penetration strategy models under uncertain information and the consistency of decision stability. Through simulations and experiments, we verified that the penetration strategy based on the pre-sampling PPO method improves the penetration effect by 15.44 % on average and the hit rate by 18.48 %. Moreover, the probability of penetration in different scenarios exceeds 70 %, and the penetration effect is improved by more than 10 % in the case of multiple interceptors. This paper also analyzes the impact of interceptor spatial distribution and the quantity of interceptors on the effectiveness of UAV penetration decision-making.

Hierarchical aerial offload computing algorithm based on the Stackelberg-evolutionary game model

Aerial access networks have been envisioned as a promising 6 G technology to enhance the service experience in underserved areas where terrestrial base stations do not exist. In such scenarios, a hierarchical model of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) is considered to provide aerial computing services for ground Internet of Things (IoT) devices. In this study, we investigate a hierarchical aerial computing system to optimally orchestrate the limited computation resources in both HAPs and UAVs. For offloading services, we formulate a joint resource allocation problem to maximize service satisfaction for terrestrial IoT devices. To solve this problem, we employ the ideas of game theory with centralized decision and decentralized execution. Through the Stackelberg-evolutionary game model, the HAP works as a leader, and selects its price strategy based on the evolutionary learning process. As followers, individual UAVs make decisions to partially offload their computing tasks by considering different objectives. According to the interactive control paradigm, our proposed method can get reciprocal advantages for HAPs, UAVs, and ground IoT devices while adaptively handling dynamic aerial network conditions. Finally, extensive simulation results verify the efficiency of our proposed algorithm to increase the usability of edge servers’ computational resources. Compared with other existing state-of-the-art aerial network offloading protocols, we can improve the profits of system throughput, resource usability and UAV fairness up to 10 %, 10 %, and 15 %, respectively.

Optimisation of Multi-Type Logistics UAV Scheduling under High Demand

At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.

Enhancing security control in Markov jump networked systems against DoS attacks: A dynamic-memory based event-triggered mechanism

This study addresses the dynamic-memory-based event-triggered H∞ control problem for Markov jump networked systems under denial-of-service attacks. By utilizing a unified approach, the characteristics of denial-of-service attacks are comprehensively captured and incorporate constraints on attack duration. Striking a balance between system performance and triggering frequency, we propose an enhanced dynamic-memory-based event-triggered mechanism. The incorporation of multiple Lyapunov functions establishes robust constraints, ensuring asymptotic mean-square stability and H∞ noise attenuation performance for closed-loop Markov jump networked systems. Simulation results using an unmanned aerial vehicle model show that the proposed controller is effective against denial-of-service attacks, and the performance is significantly improved compared to the other two research results.

Enhanced robust adaptive flight control for a convertible VTOL UAV

In this paper, we propose less conservative formulations for candidate controllers of robust adaptive mixing control (RAMC) strategies. The RAMCs are employed to solve the trajectory tracking problem throughout the full flight envelope, with guaranteed stability, of a convertible plane (CP) vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). Initially, the multi-body nonlinear dynamic model of the CP VTOL UAV is obtained using the Euler–Lagrange formalism, from which a linear parameter-varying (LPV) model is derived to be used in the design of the RAMC candidate controllers. The new formulations of the candidate controllers are proposed considering two approaches: a (i) Parallel Distributed Compensation (PDC); and (ii) a parameter-dependent Lyapunov function. Hardware-In-the-Loop (HIL) experiments are performed in a high-fidelity flight simulator to verify the fulfillment of real-time constraints, ensuring a computationally lightweight control strategy ready for implementation in a low-cost embedded computational system.

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique

While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.

A case study on the modeling and simulation of UAVs

The current work presents the flow and structural analysis of the application design in un-manned aerial vehicles (UAVs) as well as indicates a case of the modeling and simulation study with the ANSYS Fluent and Mechanical programs. This research reveals the unmanned aerial vehicle’s structural and mechanical design, structure configurations, energy-flow and struc-tural analysis, propulsion and firing systems, prototype production and testing, and design flow models. This study aims to complete the unmanned aerial vehicle design by determining its aerodynamic configurations. Due to the complexity of the design, a preliminary prepa-ration for flow analysis is performed with simplified geometry as well as flow analysis. The unmanned aerial vehicle is tested at different velocities by numerical analysis. In addition, different density flow analyses provide predictions about the aerodynamic forces of the UAVs at different heights and temperatures. The thrust results are 4240 g, power became 1711.62 W with 2.48 g/W efficiency, and 12179 [rpm] revolution for 22.2 V voltage and 77.1 A current, respectively. The 5 different analyses are performed in the range of 2.9-12 million elements, and the solution meshes with the lowest number of elements by performing parametric stud-ies with the ANSYS program that gives the most accurate result.

Research on trajectory tracking control of ocean unmanned aerial vehicles based on disturbance observer and nonlinear sliding mode

This paper aims to solve the problem of real time tracking of unmanned aerial vehicle (UAV) with external disturbances, model uncertainty, and multiple control parameter tuning during wave glider (WG) navigation. Firstly, the dynamic model of UAV and WG is instituted; Secondly, an Expanded State Observer (ADOB) is proposed to estimate the total perturbations in real time, including model uncertainties and external wind perturbations; Moreover, the ADOB is integrated with a nonlinear integral sliding mode control to generate a nonlinear sliding mode controller (ASMC) with a compound disturbance observer for the trajectory tracking control strategy; Finally, the bandwidth parameterization theory is used to tune the parameters of the ASMC controller. Simulation and experimental results show that the UAV trajectory tracking landing control scheme proposed in this paper can effectively solve the real-time tracking problem of the UAV during WG navigation.

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

A novel approach for trajectory tracking control of an under-actuated quad-rotor UAV

A novel Udwadia-Kalaba approach for the trajectory tracking control of an under-actuated quad-rotor unmanned aerial vehicle (UAV) is presented. Compared to standard control approaches, the desired trajectories are treated as constraints called trajectory tracking constraints in this approach. Neither making any approximations or linearization of the nonlinear system nor imposing any a priori structure on the nature of the nonlinear controller, this methodology provides closedform nonlinear control. The control inputs satisfying the desired trajectory requirements can be obtained explicitly in compact closed form by solving Udwadia-Kalaba equation. Nonlinear dynamics modeling of a quad-rotor UAV is processed and a desired trajectory is given in this paper to illustrate this approach. The theoretical analysis and MATLAB simulation results verify the validity and efficiency of this approach. The real-time servo constraint forces are obtained conveniently and the quad-rotor UAV's movement meets the designed trajectory precisely.

Control of High-altitude Power Grid Inspection UAV Using Hierarchical PID Based on Reinforcement Learning

Unmanned aerial vehicles (UAVs) are now being utilized in a wide range of work scenarios to replace humans in performing dangerous tasks and jobs. In the context of high-altitude power grid inspections, UAV inspections have significant advantages. However, the complex environment often poses challenges for controlling UAVs. Building upon traditional PID control, this paper uses MATLAB software to construct the dynamic model and controller of a quadcopter UAV. A cascaded loop is established by selecting the control objects for the inner and outer loops. The stabilization of the UAV is achieved by setting the PID parameters for the inner and outer loops. Additionally, the deep reinforcement learning algorithm is employed to fine-tune the PID parameters based on the Zigler-Nicholes method. The adjustment is performed by considering the reward function values for the UAV under different disturbances. Finally, response curves and dynamic response parameter indicators are obtained. Compared to traditional cascaded PID control, the quadcopter UAV system tuned using this method exhibits significant improvements in both dynamic response parameters and steady-state responses.

A Co-Adaptation Method for Resilience Rebound in Unmanned Aerial Vehicle Swarms in Surveillance Missions

An unmanned aerial vehicle (UAV) swarm is a fast-moving system where self-adaption is necessary when conducting a mission. The major causative factors of mission failures are inevitable disruptive events and uncertain threats. Given the unexpected disturbances of events and threats, it is important to study how a UAV swarm responds and enable the swarm to enhance resilience and alleviate negative influences. Cooperative adaptation must be established between the swarm’s structure and dynamics, such as communication links and UAV states. Thus, based on previous structural adaptation and dynamic adaptation models, we provide a co-adaptation model for UAV swarms that combines a swarm’s structural characteristics with its dynamic characteristics. The improved model can deal with malicious events and contribute to a rebound in the swarm’s performance. Based on the proposed co-adaptation model, an improved resilience metric revealing the discrepancy between the minimum performance and the standard performance is proposed. The results from our simulation experiments show that the surveillance performance of a UAV swarm bounces back to its initial state after disruptions happen in co-adaptation cases. This metric demonstrates that our model can contribute towards the swarm’s overall systemic resiliency by withstanding and resisting unpredictable threats and disruptions. The model and metric proposed in this article can help identify best practices in improving swarm resilience.

Geoinformation modeling of city lawns

The article describes the possibility of using geoinformation modeling technology to create a three-dimensional model of a lawn area based on the formation of an accurate, detailed component geoinformation micro-model. The importance of lawn as an indicator of sustainable urban environment is described. The process of modeling on the basis of the obtained data set with the use of unmanned aerial vehicle and three-dimensional modeling environment Blender is revealed step by step. The possibilities of development of the technology of geoinformation modelling of lawns are pointed out, with a description of specific recommendations for improving the methodology proposed by the authors.

A visual positioning model for UAV’s patrolling video sequence images based on DOM rectification

With technological development of multi sensors, UAV (unmanned aerial vehicle) can identify and locate key targets in essential monitoring areas or geological disaster-prone areas by taking video sequence images, and precise positioning of the video sequence images is constantly a matter of great concern. In recent years, precise positioning of aerial images has been widely studied. But it is still a challenge to simultaneously realize precise, robust and dynamic positioning of UAV’s patrolling video sequence images in real time. In order to solve this problem, a visual positioning model for patrolling video sequence images based on DOM rectification is proposed, including a robust block-matching algorithm and a precise polynomial-rectifying algorithm. First, the robust block-matching algorithm is used to obtain the best matching area for UAV’s video sequence image on DOM (Digital Orthophoto Map), a pre-acquired digital orthophoto map covering the whole UAV’s patrolling region. Second, the precise polynomial-rectifying algorithm is used to calculate accurate rectification parameters of mapping UAV’s video sequence image to the best matching area obtained above, and then real time positioning of UAV’s patrolling video sequence images can be realized. Finally, the above two algorithms are analyzed and verified by three practical experiments, and results indicate that even if spatial resolution, surface specific features, illumination condition and topographic relief are significantly different between DOM and UAV’s patrolling video sequence images, proposed algorithms can still steadily realize positioning of each UAV’s patrolling video sequence image with about 2.5 m level accuracy in 1 s. To some extent, this study has improved precise positioning effects of UAV’s patrolling video sequence images in real time, and the proposed mathematical model can be directly incorporated into UAV’s patrolling system without any hardware overhead.

An Adaptation of a Sliding Mode Classical Observer to a Fractional-Order Observer for Disturbance Reconstruction of a UAV Model: A Riemann–Liouville Fractional Calculus Approach

This paper proposes a modification of a Sliding Mode Classical Observer (SMCO) to adapt it to the fractional approach. This adaptation involves using a set of definitions based on fractional calculus theory, particularly the approach developed by Riemann–Liouville, resulting in a Sliding Mode Fractional Observer (SMFO). Both observers are used to perform disturbance reconstruction considered additive in a Quadrotor Unmanned Aerial Vehicle (UAV) model. Then, this work presents the fractional-order sliding mode observer’s mathematical formulation and integration into the Quadrotor UAV model. To validate the quality of the disturbance reconstruction process of the proposed SMFO observer scheme, numerical simulations are carried out, where a reconstruction quality indicator (BQR) is proposed based on the analysis of performance indices such as the Mean Square Error (MSE), the First Probability Moment (FPM), and Second Probability Moment (SPM), which were obtained for both the SMCO and the SMFO. The simulation results demonstrate the efficacy of the proposed observer in accurately reconstructing disturbances under various environmental conditions. Comparative analyses with SMCO highlight the advantages of the fractional-order approach in terms of reconstruction accuracy and improvement of its transitory performance. Finally, the presented SMFO offers a promising avenue for enhancing the reliability and precision of disturbance estimation, ultimately contributing to the advancement of robust control strategies for Quadrotor UAV systems.