Unmanned Aerial Vehicle Flight Control Research Articles

Optimization of Unmanned Aerial Vehicle Flight Control Sensor Control System based on Deep Learning Model

Based on data modelling strategies have created reliable classifier designs for various classes and other neural network applications. The fact that modelling complexity rises with the total number of groups in the system does is one of the approach's major shortcomings. No matter how well it performs, it could make the classifier's design ugly. This article discusses the development of a novel, logic-based Optimum Bayesian Gaussian process (OBGP) classifier to reduce the number of separate empirical models required to accurately detect various fault types in industrial processes. The precision of the OBGP classifier's defining faults also contrasts with the results of other approaches documented in the literature.

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

A Deep Neural Network-Fused Mathematical Modeling Approach for Reliable Flight Control of Small Unmanned Aerial Vehicles

In order to ensure the flight safety of small unmanned aerial vehicles (UAVs), a deep neural network-fused mathematical modeling approach is put up for reliable flight control of small UAVs. First, engine torque, thrust eccentricity and initial stop angle are taken into full consideration. A six-degree-of-freedom nonlinear model is formulated for small UAVs, concerning both ground taxiing and air flight status. Then, the model was linearized using the principle of small disturbances. The linearized model expressions for both ground taxiing and air flight were provided. In addition, radial basis function neural networks are used for online approximation to address the nonlinearity and uncertainty caused by changes in aircraft aerodynamic parameters. At the same time, to compensate for the external disturbance and the approximation error of the neural network, the system robustness is improved by selecting reasonable design parameters. This helps the whole flight control system obtain better tracking control performance. At last, some simulation experiments are carried out to evaluate the performance of the proposed mathematical modeling framework. The simulation results show that the proposal has stronger convergence ability, smaller prediction error, and better performance. Thus, proper proactivity can be acknowledged.

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.

Real Time Route Adjustment of a UAV Based on Dust Measurement with an Onboard Sensor

This research is driven by a pressing requirement to improve the effectiveness and safety of unmanned aerial vehicle (UAV) operations, specifically in atmospheric particle sensing. Airborne particulates present a substantial health hazard, leading to an increasing demand for inventive solutions capable of autonomously guiding UAVs along optimal routes, steering clear of regions with heightened dust concentrations. This research aims to address this challenge by developing a sophisticated approach that integrates atmospheric particle sensors with UAV flight control systems. The approach outlined in this study relies on atmospheric sensors mounted on a UAV to measure dust levels in a specified region. The UAV, following a predetermined route over the area, detects dust pollution. It dynamically adjusts its route based on the observed dust levels, avoiding areas with high concentrations of dust. This adaptive route determination aims to identify safe paths, avoiding regions with elevated dust levels that may pose risks to human health. The flight strategy and area-scanning methodology tailored for this objective are established, enabling the UAV to execute the assigned task. The ultimate goal is to create a system that not only minimizes energy consumption but also prioritizes human health by autonomously redirecting the UAV away from potentially harmful dust levels. The real-time monitoring and telemetry data feedback mechanisms further contribute to the advancement of UAV technology for environmental sensing and risk mitigation. Additionally, the integration of the laser-based dust sensor, i.e., Gp2y10, with the ArduPilot autopilot flight computer ensures seamless coordination, with the UAV adjusting its flight path based on the sensor's output.

Оценка влияния непреднамеренных помех на линию вверх канала управления беспилотного летательного аппарата

Modern unmanned aerial vehicles (UAVs) are becoming increasingly important in solving many problems, for example, when delivering goods, video recording, conducting aerial monito¬ring of the condition of remote and hard-to-reach objects, etc. When solving these problems, UAVs may experience unintentional interference from other radio-electronic equipment (RES). Currently, virtually no attention is paid to issues related to the analysis of the quality of operation of civilian UAVs in the presence of unintentional interference from other civil electronic distribution systems. The proposed method is intended to somewhat fill this gap. It is based on the assumption that a decrease in the quality of reception of useful signals in the radio control channel below a certain value due to the low signal-to-noise ratio at the input of the UAV receiver in its bandwidth will lead to errors in the UAV flight control commands and the wrong starting of on-board specialized equipment. The level of the interfering signal delivered to the UAV receiver from a stationary land-based transmitter depends on interaction areas between transmitter and receiver antenna patterns. A method for determining these areas, consisting of main and side lobes of antenna patterns, is proposed. This method can be used to solve the more general problem of analyzing the electromagnetic compatibility of UAV radio stations, control panels and ground-based electronic devices, as it is an integral part of solving this problem.

Path planning for dual UAVs cooperative suspension transport based on artificial potential field-A* algorithm

Unmanned Aerial Vehicles (UAVs) are frequently utilized for transferring and delivering. Dual- or multi-quadrotor cooperative transportation schemes are required for heavy loads. In this work, focusing on the path planning for a dual-quadrotor cooperative transport system, a unique artificial potential field-A*(APF-A*) algorithm is studied to fulfill the safe and effective cooperative suspension transport. APF-A* algorithm proposed the strategy of host-wingman cooperative transportation based on the APF theory, and optimizes the heuristic function by introducing the concept of repulsive force to maintain the quadrotor flying at a safe distance from the obstacle. In addition, APF-A* algorithm promises to increase search efficiency while identifying the optimal path, and it can reduce the swing angle motion of loads to enhance the security of transportation. The coordinated flight control and path planning of quadrotor UAVs are simulated in AirSim environment and tested in flight experiments. The results demonstrate that the algorithm is capable to plan safe and reliable paths, meanwhile changing formation and avoid collision when the wingman is close to obstacles. In comparison to the regular A* algorithm, the APF-A* algorithm proposed in the paper performs better in path planning for dual-quadrotor cooperative transport system, reducing the search time and the number of search grids by nearly 60% and 53.75%, respectively.

Modular Reinforcement Learning for Autonomous UAV Flight Control

Recently, research on unmanned aerial vehicles (UAVs) has increased significantly. UAVs do not require pilots for operation, and UAVs must possess autonomous flight capabilities to ensure that they can be controlled without a human pilot on the ground. Previous studies have mainly focused on rule-based methods, which require specialized personnel to create rules. Reinforcement learning has been applied to research on UAV autonomous flight; however, it does not include six-degree-of-freedom (6-DOF) environments and lacks realistic application, resulting in difficulties in performing complex tasks. This study proposes a method of efficient learning by connecting two different maneuvering methods using modular learning for autonomous UAV flights. The proposed method divides complex tasks into simpler tasks, learns them individually, and then connects them in order to achieve faster learning by transferring information from one module to another. Additionally, the curriculum learning concept was applied, and the difficulty level of individual tasks was gradually increased, which strengthened the learning stability. In conclusion, modular learning and curriculum learning methods were used to demonstrate that UAVs can effectively perform complex tasks in a realistic, 6-DOF environment.

Augmentation for unmanned aerial vehicle position estimation using MARG and optical flow sensors

The optical flow sensor can detect the movement of the unmanned aerial vehicle (UAV) on the ground; thus, it is widely used for UAV flight control. The commonly used pinhole model of optical flow sensor describes the optical flow measurement in linear and angular velocities. It is not suitable in the case of discrete-time processing. A novel measurement model for the optical flow sensor is proposed, which directly gives the relationship between the optical flow and the UAV’s translational/angular motions in each sampling period. A data fusion scheme based on cubature transform is also presented, which can augment UAV’s position estimation using optical flow data. The proposed method is proven to be effective through flight tests on UAVs.

Fault-Tolerant Attitude Tracking Control Driven by Spiking NNs for Unmanned Aerial Vehicles.

In this article, we proposed a novel fault-tolerant control scheme for quadrotor unmanned aerial vehicles (UAVs) based on spiking neural networks (SNNs), which leverages the inherent features of neural network computing to significantly enhance the reliability and robustness of UAV flight control. Traditional control methods are known to be inadequate in dealing with complex and real-time sensor data, which results in poor performance and reduced robustness in fault-tolerant control. In contrast, the temporal processing, parallelism, and nonlinear capacity of SNNs enable the fault-tolerant control scheme to process vast amounts of sensory data with the ability to accurately identify and respond to faults. Furthermore, SNNs can learn and adjust to new environments and fault conditions, providing effective and adaptive flight control. The proposed SNN-based fault-tolerant control scheme demonstrates significant improvements in control accuracy and robustness compared with conventional methods, indicating its potential applicability and suitability for a range of UAV flight control scenarios.

Attitude Estimation of Four-Rotor UAV Based on Extended Kalman Filter

Quadrotor unmanned aerial vehicle (UAV) is a typical multi-input multi-output (MIMO), nonlinear and strong coupling underactuated system. In the working process of the system, it is necessary to perform information fusion on the attitude detected by the sensor to achieve accurate measurement of attitude angle and angular velocity. Accurate and efficient measurement of UAV attitude angle is the basis of UAV flight control. In this paper, the extended Kalman filter (EKF) algorithm is used to estimate the attitude information of the four-rotor UAV. Firstly, a four-rotor UAV simulation model is established on Simulink in Matlab, and then the attitude information of the UAV is measured and estimated. The results show that the extended Kalman filter algorithm can effectively estimate the attitude information of UAV.

Modeling and Flight Control of Small UAV with Active Morphing Wings

In recent research works, morphing wings were studied as an interesting field for a small unmanned aerial vehicle (UAV). The previous studies either focused on selecting suitable material for the morphing wings or performing experimental tests on UAVs with morphing wings. Though, the dynamic modeling of active flexible morphing wings and their involved interactions with the aerodynamics of the UAV body are challenging subjects. Using such a model to control a small UAV to perform specific maneuvering is not investigated yet. The dynamic model of UAV with active morphing wings generates a multi-input multi-output (MIMO) system which rises the difficulty of the control system design. In this paper, the aeroelastic dynamic model of morphing wing activated by piezocomposite actuators is established using the finite element method and modal decomposition technique. Then, the dynamic model of the UAV is developed taking into consideration the coupling between the wing and piezocomposite actuators, as well as the dynamic properties of the morphing actuators with the aerodynamic wind disturbances. A model predictive control (MPC) is designed for the MIMO control system to perform specific flight maneuvering by tracking desired trajectories of UAV altitude and yaw angle. Additionally, the MPC achieves constrained behavior of pitch and roll angles to get satisfactory UAV motion. Also, the behaviors of the UAV control system using MPC are evaluated after adding Dryden wind turbulence to the UAV outputs. Finally, a UAV flight simulation is conducted which shows that the control system successfully rejects the applied disturbances and tracks the reference trajectories with acceptable behavior of pitch and roll angles.

Tracking flight control of UAV based on multiple regions pole assignment method

This paper presents a tracking control problem for an unmanned aerial vehicle (UAV) using a multiple regions pole assignment method. To obtain the UAV control gains under the regional pole assignment method, the traditional robust control approach is to construct a closed-loop system and a specific linear matrix inequality (LMI) region in a complex plane, all poles in the system are configured in this specific region by solving LMI. Since the given LMI region is fixed with a large scale, further constraints on the LMI region can make poles configuring in a more appropriate position to obtain better control effects. Hence, based on the UAV tracking control problem, a multiple regions pole assignment method is proposed in this paper. By designing multiple LMI regions and a novel solving algorithm for pole assignment, the optimized tracking control scheme can be obtained. A practical example of quadrotor UAV tracking is given to illustrate the effectiveness of the proposed method.

Coordinated Disturbance Observer-Based Flight Control of Fixed-Wing UAV

In this brief, a novel coordinated disturbance observer (CDO) is proposed to tackle the disturbance which affects the stable flight of a fixed-wing unmanned aerial vehicle (UAV). The above CDO is composed of multiple disturbance observers which have a basic form and depend on the designed coordinated variables. Due to the above special structure of the CDO, for one thing, the requirement of positive definiteness of the observer gain posed on a single disturbance observer is removed in the CDO. For another, the performance of the CDO can be enhanced by weighting multiple small disturbance observer gains. Via the Lyapunov function method, not only the decay rate and stable region of disturbance estimation error (DEE) of the CDO for the fixed-wing UAV are analyzed, but also the stability criteria of DEE and tracking error are given. Finally, the effectiveness of the proposed CDO is verified through the simulation of flight control of a fixed-wing UAV.

Autonomous unmanned aerial vehicle flight control using multi-task deep neural network for exploring indoor environments

Autonomous unmanned aerial vehicle flight control using multi-task deep neural network for exploring indoor environments

Flight Control System Simulation for Quadcopter Unmanned Aerial Vehicle (UAV) based on Matlab Simulink

Unmanned aerial vehicle (UAV) are widely used in military and civilian applications due to its advantages of strong maneuverability. Flight control system simulation of a UAV is always a research focus. At present, there are three major algorithms for a UAV flight control: strapdown inertial navigation system, Kalman filter algorithm, flight control Proportional-Integral-Derivative (PID) algorithm. In this study, UAV flight control system and quadcopter drone flight control principle are discussed. In addition, this research developed a dynamic model based on a thorough examination of the quadcopter drone’s frame construction and features. This dynamic model describes the transformation matrix of UAV coordinate system to ground coordinate system. Matlab Simulink was used to simulate a flight control system. A quadcopter drone’s vertical movement, yaw motion, pitching motion, and roll motion, as well as its flight attitude, were simulated and described. Dynamic modeling is developed based on the drone’s flight principle and force relationship in different motion states, as well as the Newton-Eulerian model. Matlab Simulink was used to simulate the trajectory of a quadcopter drone. The red line denotes the trajectory, while the black x markings denote a change in trajectory or a specific position. Blue lines that reflect the waypoint’s heading accompany specific poses. The results of this study could provide a good reference for the simulation of a UAV flight control system.

Dual-loop Hovering Control for Unmanned Helicopter

The recent development of unmanned aerial vehicle flight control creates a strong demand for higher control accuracy and control quality. In this paper, the precise control and stable hovering problem are studied for unmanned helicopters. To solve the problem, we propose a hovering controller for dual-loop of unmanned helicopters. Firstly, the nonlinear mathematical model of unmanned helicopters is established based on Newton-Euler’s laws. Then, we respectively design the position model and attitude model. In the position model, we use the cascade PD controller to realize the precise control; In the attitude model, we use the cascade PD controller to realize the stable hovering. Simulation results show the effectiveness of the conclusions made in this paper.

Unmanned Aerial Vehicles: Control Methods and Future Challenges

With the rapid development of computer technology, automatic control technology and communication technology, research on unmanned aerial vehicles (UAVs) has attracted extensive attention from all over the world during the last decades. Particularly due to the demand of various civil applications, the conceptual design of UAV and autonomous flight control technology have been promoted and developed mutually. This paper is devoted to providing a brief review of the UAV control issues, including motion equations, various classical and advanced control approaches. The basic ideas, applicable conditions, advantages and disadvantages of these control approaches are illustrated and discussed. Some challenging topics and future research directions are raised.

Local Control of Unmanned Air Vehicles in the Mountain Area

The task of increasing the accuracy and stabilization of the flight of unmanned aerial vehicles (UAV) in the alpine environment is a complex problem. It is related to the evaluation of UAV flight parameters and control conditions for the operator’s place. The purpose of the UAV’s autonomous flight control is to ensure stable control of the UAV’s flight parameters. Flight control systems are affected by various disturbances caused by both internal and external conditions. In general, the number of autonomous control systems corresponds to the number of degrees of freedom, which determines the flight of an autonomous vehicle. An important factor in assessing the quality of such a UAV is its readiness for an autonomous flight together with the level of its safe guidance on the route. The presented article focuses on the analysis of UAV flight control and the quality of prediction and elimination of errors that exist during maneuvers toward the place of a successful UAV landing. The aim of the article is to point out the solvability of the complexities of such a flight procedure with the evaluation of the readiness for the descent phase of the autonomous UAV. The given problem is caused by the social demand for the creation of a way of providing health care in the mountain area of the High Tatras in Slovakia. The existing database of data obtained from the flying vehicles used in Slovakia was compared with the data obtained from the simulated flights, with their subsequent evaluation in the MATLAB software (Version R2021b) environment.

Architectural Process for Flight Control Software of Unmanned Aerial Vehicle with Module-Level Portability

To apply UAVs (Unmanned Aerial Vehicle) into different fields, including research and industry, and expand it quickly, reliable but modular software is required. The existing flight control software (FCS) of the UAV consists of various types of modules categorized into different layers, and it is responsible for coordinating, monitoring, and controlling the vehicle during its flight. This paper proposes mpFCS, a structure of UAV flight control software, which provides portability to its modules and is easy to expand. The mpFCS consists of four segments and several modules within the segments. mpFCS provides portability for each module within the segment. Existing software does not provide portability for its modules because of the tight coupling resulting from its different and private interfaces. The mpFCS uses interfaces of the standard airborne software architecture to transfer data between its modules. Moreover, the structure provides portability for its modules to run in the standard airborne software environment. In order to verify the mpFCS, we tested the mpFCS with the conformance test suite of the airborne software that provides the testing environment for the interfaces and modules of the software. The mpFCS passed the test. Test results show that all modules of the mpFCS are portable. Additionally, portable modules can be interoperable with other software, and the structure is expandable with new modules that use standard airborne software interfaces.