UAV Dynamics Research Articles

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.

Three-Dimensional Spatial Atmospheric Turbulence Generation Method for Aerial Refueling Flight Simulation

Traditional flight simulation models often operate on the premise of a steady atmosphere, overlooking the complexities of actual atmospheric dynamics and the flight safety risks posed by wind disturbances, such as turbulence. To Address this oversight, the present study introduces a method for generating three-dimensional atmospheric turbulence based on spatial correlation functions. This method, rigorously validated against correlation and spectral benchmarks, guarantees isotropic properties in the synthesized turbulence fields. Through interpolation techniques, the model integrates the spatial atmospheric turbulence into the flight simulation framework effectively. The paper highlights the application of this model by examining the impact of atmospheric turbulence on the precise flight dynamics of quadcopter UAVs during aerial refueling operations. The findings demonstrate the model’s pertinence not only to UAVs but also to the broader spectrum of aircraft and their operational procedures.

Research on the Effect Characteristics of Free-Tail Layout Parameters on Tail-Sitter VTOL UAVs

The tail-sitter VTOL UAV boasts not only high-speed cruising and air hovering capabilities, but also its unique tail-sitting vertical takeoff and landing and hovering attitude enable aerial operations with an exceptionally small cross-sectional area. This feature effectively broadens the scope of application for the UAV in intelligent agriculture, encompassing tasks such as agricultural inspection, production monitoring, and topographic mapping. Given the necessity for frequent modal transitions, this paper is grounded in a thorough examination of the typical structural characteristics of the tail-sitter VTOL UAV. A comprehensive technical solution for tail-sitter VTOL UAVs, based on the free-tail configuration, is proposed in this paper. The free-tail structure is utilized to address the limitations of traditional tailless layout and fixed landing gear in terms of flight stability and takeoff/landing performance of tail-sitter VTOL UAVs. However, the implementation of this solution necessitates the addition of a new maneuvering unit. Consequently, this paper delves into the aerodynamic coupling characteristics and laws between the layout parameters such as tail number, tail length, and tail area and the tail-sitter VTOL UAV fuselage. To optimize the free-tail configuration, a multi-objective optimization is performed by integrating the overall UAV dynamics, landing dynamics, and modal transition trajectory constraints. The results of stability modeling simulations and flight tests demonstrate that the tail-sitter VTOL UAV equipped with this technical solution exhibits enhanced maneuverability and flight efficiency compared to the conventional tailless layout.

Robust GDI-based adaptive recursive sliding mode control (RGDI-ARSMC) for a highly nonlinear MIMO system with varying dynamics of UAV

Robust GDI-based adaptive recursive sliding mode control (RGDI-ARSMC) for a highly nonlinear MIMO system with varying dynamics of UAV

Research on PID Control of Quadrotor Drones Based on MATLAB

Four-rotor UAV is a very practical and widely used UAV. In this paper, the development status and future trend of Fourrotor UAV at home and abroad are introduced, and then the transformation matrix from ground coordinates to airframe coordinates is deduced by using Euler equation. In the process of dynamic modeling, the plane position reference coordinate system and rotation angle reference coordinate system are selected to analyze the external forces and moments on the body, and the linear motion equation and the angular motion equation are written in parallel. On the basis of UAV dynamics model, the classical PID control method is used to control the attitude of the inner loop and the position of outer loop . According to simulation results, the flight controller can keep the UAV flying stably.

Optimal Control for a Three-Rotor Unmanned Aerial Vehicle in Programmed Flights

In this study, we propose a new approach to selecting PID controller parameters for a UAV tricopter during programmed flights. The approach uses optimization techniques to determine the optimal values of the PID controller parameters based on the desired performance of the UAV. The proposed method is particularly effective for repeated programmed flights in which the UAV follows a defined flight path. The use of a PID control provides a reliable and robust control system that can cope with various disturbances and uncertainties in UAV dynamics. The proposed method provides an alternative to adaptive control, which requires a significant amount of system identification and parameter tuning. The effectiveness of the proposed method has been verified in simulation studies, and the results show its ability to achieve satisfactory performance with low tracking error and fast response time. The result of the work is an improvement in control for a specific object at a specific mission. We present how the recipients can perform this procedure for their object and their mission to be able to improve the control gain in the physical controller.

Attitude Control of Quadrotor UAV Using Improved Active Disturbance Rejection Control

The quadrotor UAV has complex aerodynamics, unstable altitude, and easy to be disturbed by internal and external uncertainties during flight, so good attitude control becomes a challenge, this paper improves the active disturbance rejection controller to improve the system's anti-jamming ability and response speed in response to the above problems. Firstly, a quadrotor UAV dynamics model is established; then the Ifal function is constructed, which solves the problems that the inflection point of the fal function is not smooth, which is easy to cause the system jitter and the system gain is large when the error is large, and finally the performance is verified by using MATLAB/Simulink simulation software. The simulation results show that the constructed Ifal function is smoother and has better convergence, and compared with the traditional active disturbance rejection control, the improved active disturbance rejection control system has faster response speed, stronger anti-jamming ability and robustness in the attitude control of quadrotor UAV.

Accurate Obstacle Avoidance for Quadcopter UAVs under Moving Obstacle Conditions based on A* Algorithm

Aiming at the problem that UAVs are easy to interfere with the normal activities of animals when observing wild animals, an A*-based algorithm is proposed, and through the improvement of the A* algorithm, it realises the accurate obstacle avoidance of the quadcopter UAV under the moving obstacles to ensure that it can effectively complete its tasks in the field. Firstly, the UAV kinematic model is established by analysing the UAV dynamics; secondly, the traditional A* algorithm is introduced and improved, and the improved A* algorithm is able to autonomously regulate the distance maintained with obstacles, which is suitable for observing different kinds of animals in the field; finally, the code is written for the algorithm and the moving path of the UAV is derived under the condition of maintaining different distances from obstacles, which shows the improved algorithm's The feasibility of the improved algorithm is shown.

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.

Modelling and Attitude Control of an Agile Fixed Wing UAV based on Nonlinear Dynamic Inversion

An estimation model is key in the design and development of an unmanned aerial vehicle's control system. This work presents a complete methodology for modelling the dynamics of a fixed-wing UAV for aerobatic maneuvers. The UAV dynamic non-linear model considered uses total variables instead of a nominal values and perturbation values about certain trimmed conditions for conventional flight envelopes. Such modelling allows the expansion of the flight envelope of an unmanned aerial vehicle to cover the full range of the angle of attack. The quaternion formulation is used since it eliminates the nonlinearity of the aerodynamics due to Euler angles for high angle of attacks. The objective is to have a complete and accurate representation of a highly dynamic fixed wing UAV capable of making aerobatic maneuvers without encountering singularities. A set of controllers are then designed for the inner and outer loops for attitude control using nonlinear dynamic inversion in cascade with a PI controller. Simulations carried out show robust tracking of reference attitude angles.

A new search scheme using multi‐bee‐colony elite learning method for unmanned aerial vehicles in unknown environments

AbstractIn this research, a cooperative search for multiple dynamic targets in an unknown marine environment by multiple unmanned aerial vehicles is studied based on a novel multi‐bee‐colony (MBC) elite learning algorithm. First, a specialized searching model is established which includes the UAV dynamics, the sensor model, the target probability and environmental certainty at different flight altitudes. Then, a new search strategy, which consists of rough search and accurate search, is proposed by maximizing the multiobjective utility function with the dynamic changing of the flight altitude. In order to solve the optimization problem, an improved MBC algorithm based on elite learning is designed, which can improve the adaptability and computation speed of the standard artificial bee colony (ABC) algorithm under different search missions. Finally, extensive simulations are conducted to show the effectiveness and superiority of the proposed search strategy.

Sliding mode disturbance observer and sliding mode controller for quadrotor UAV

The dynamics of quadrotor UAV is described considering internal uncertainty and external disturbance. The control system of quadrotor UAV is divided into inner loop attitude control system and outer loop position control system, In order to solve the problem of underactuated character. Three virtual control variables are introduced to decouple the position dynamics. Three sliding mode disturbance observers (SMDOs) are designed to estimate the total disturbances in the position dynamics. Then three sliding mode controllers (SMCs) are designed to drive the trajectory tracking errors to zero asymptotically. The simulation example is carried out to test the effectiveness of SMDOs and SMCs. Simulation results validate that the designed SMDOs and SMCs provide both fast exact tracking capability and strong disturbance rejection capability for the quadrotor UAV.

Model predictive control for automatic carrier landing considering ship motion

This paper studies a control problem of UAV automatic landing on carrier considering carrier heave motion, which is given by an empirical model based on spectral density function. A linearized UAV dynamics model is applied as the predictive model, a MPC controller is then proposed for the flight dynamics control. Simulation is conducted with real parameters of the UAV “Silver Fox”, the results exhibit the great landing performance of MPC method.

Cloud DEVS-based computation of UAVs trajectories for search and rescue missions

ABSTRACT This paper presents a new Cloud-deployable DEVS-based framework for optimising UAV trajectories and sensor strategies in target-search missions. DEVS provides it with a well-established, flexible, and verifiable modelling strategy to include different models for the UAV, sensor, and target dynamics; the target and sensor uncertainty; and the optimising process. Its Cloud deployability speeds up the evaluations/simulations required to optimise this NP-hard problem, which involves computationally heavy models when solving real-world missions. The framework, designed to handle different types of target-search missions, currently optimises, using a multi-objective Genetic Algorithm, free-shape trajectories of multiple UAVs,eqquiped with several static/movable sensors to detect a target within a search area. It is implemented in xDEVS and deployable over a set of containers in the Google Cloud Platform. The results show that our deployment policy speeds up the computation up to 3.35 times, letting the operator simultaneously optimise several search strategies for agiven scenario.

A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs

Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.

Automated Path Planning for unmanned aerial vehicle in Urban Dynamic Area

AbstractThis paper addresses the path planning and autonomous obstacle avoidance problem of UAVs in urban dynamic area. A flight path planning strategy for UAVs in complex urban environments is proposed.First, the A* algorithm is used to construct a desired global path in a 3D static environment, which is used as the static reference path for dynamic obstacle avoidance below.The environmental and the key points of algorithm are also elaborated. In this paper, the dynamic obstacles are divided into three categories, then, in order to avoid the collision between dynamic obstacles and static optimal paths, two strategies to achieve local online path adjustment are proposed. Finally, the seven order minimum snap trajectories generation based on piecewise polynomials is utilized to smooth the flight path.We can obtain a smooth trajectory based on UAV dynamics and safety. The simulation results verify the effectiveness of the proposed UAV path planning strategy in the dynamic and complex urban area.

Adaptive block backstepping control for a UAV performing lateral maneuvers under lateral c.g. uncertainty

PurposeUnmanned aerial vehicles (UAVs) have wide applications in surveillance and reconnaissance without risking human life. Due to unbalanced payload distribution or in-flight deployment, UAVs may undergo lateral center of gravity (c.g.) variations resulting in an asymmetric dynamic having significant longitudinal and lateral/directional coupling and hence more pronounced nonlinearity. Therefore, automatic control of UAVs becomes extremely difficult when it is forced to perform maneuvers under such imbalance in lateral mass distribution. The purpose of this paper is to design adaptive nonlinear control so that the UAV can perform some useful lateral/directional maneuver under lateral c.g. uncertainty.Design/methodology/approachFirst the nominal lateral/directional dynamics of a fixed-wing UAV is framed into strict feedback form and then the block backstepping approach is used to design the controller to execute horizontal turn and aileron roll maneuvers under no lateral c.g. variation. Thereafter, an adaptive block backstepping controller is designed to adapt to uncertainty in lateral c.g. position considering an approximate model of the asymmetric dynamics. The proposed adaptive scheme is validated against the same two maneuvers as considered for the nominal case.FindingsFirst it is shown that the lateral/directional dynamics of a UAV can be converted to a block strict feedback form for executing some lateral/directional maneuvers. However, it was observed that the maneuver performance suffers significant performance degradation under lateral c.g. variations. To mitigate this issue, a simple and computationally inexpensive adaptive block backstepping scheme is proposed and validated. The adaptation law is further proved to be able to asymptotically estimate the actual c.g. location of the UAV.Practical implicationsThe proposed control scheme allows the UAV to automatically adapt to lateral c.g. variations so that the intended maneuvers are performed without any noticeable loss in maneuver performance.Originality/valueThere are very few works available in the literature that address nonlinear control designs for executing specific lateral/directional maneuvers and, moreover, they consider symmetric UAVs or aircraft only. This paper addresses the practical problem of autonomous maneuvering for UAVs with unbalanced lateral mass distribution leading to shift of c.g. out of its plane of symmetry.

Real-time formation control and obstacle avoidance algorithm for fixed-wing UAVs

AbstractThis paper proposes a novel real-time formation control and obstacle avoidance algorithm for multiple fixed-wing UAVs. A formation control algorithm is designed by a combination of the virtual structure, leader-follower, and artificial potential fields methods and harnessing the advantages of those approaches. The kinematic and dynamic constraints of fixed-wing UAVs are considered in the path planning. The performance of the proposed algorithm is examined through simulation in Matlab software by applying the translational dynamics of fixed-wing UAVs. Simulations of different complex scenarios demonstrate the effectiveness of the presented formation flight algorithm through generating multiple efficient paths, which are fully consistent with the functional constraints of the UAVs in the presence of obstacles.

A SimPLE Tool for UAV Remote Pilot Training and Testing

With the ever-growing potential of using UAVs in various fields there is a need for proper training of their remote pilots. This paper presents a newly developed tool for the training and testing of UAV remote pilots, based on the measurement of their responses in controlling a simulated UAV in 2D space. The evaluation system consists of a conventional RC controller and a PC running Python scripts for the measurement and preprocessing of data and the MATLAB System Identification Toolbox for the calculation of the parameters based on the human model. Based on the initial testing results, which were carried out with an experienced pilot and a beginner, four key parameters were identified, that would be used for the evaluation of the performance of UAV remote pilots. Furthermore, the developed system could be used for training purposes to learn the basic principles and dynamics of UAVs in a safe environment.

Full-state modeling and nonlinear control of balloon supported unmanned aerial vehicle

PurposeThe purpose of this study is to provide the full-state mathematical model and devise a nonlinear controller for a balloon-supported unmanned aerial vehicle (BUAV).Design/methodology/approachNewtonian mechanics is used to establish the nonlinear mathematical model of the proposed vehicle assembly which incorporates the dynamics of both balloon and quadrotor UAV. A controllable form of the nine degrees of freedom model is derived. Backstepping control is designed for the proposed model and simulations are performed to assess the tracking performance of the proposed control.FindingsThe results show that the proposed methodology works well for smooth trajectories in presence of wind gusts. Moreover, the final mathematical model is affine and various nonlinear control techniques can be used in the future for improved system performance.Originality/valueMulti-rotor unmanned aerial vehicles (MUAVs) are equipped with controllers but are constrained by smaller flight endurance and payload carrying capability. On the contrary, lighter than air (LTA) aerial vehicles have longer flight times but have poor control performance for outdoor operations. One of the solutions to achieve better flight endurance and payload carrying capability is to augment the LTA balloon to MUAV. The novelty of this research lies in full-order mathematical modeling along with transformation to controllable form for the BUAV assembly.