Guidance and Control for UAV Aerial Refueling Docking Based on Dynamic Inversion with L_1 Adaptive Augmentation

The guidance and control for UAV aerial refueling docking based on dynamic inversion with L1 adaptive augmentation is studied.In order to improve the tracking performance of UAV aerial refueling docking,aguidance algorithm is developed to satisfy the tracking requirement of position and velocity,and it generates the UAV flight control loop commands.In flight control loop,based on the 6-DOF nonlinear model,the angular rate loop and the attitude loop are separated based on time-scale principle and the control law is designed using dynamic inversion.The throttle control is also derived from dynamic inversion method.Moreover,an L1 adaptive augmentation is developed to compensate for the undesirable effects of modeling uncertainty and disturbance.Nonlinear digital simulations are carried out.The results show that the guidance and control system has good tracking performance and robustness in achieving accurate aerial refueling docking.

LQR Integrated with Fuzzy Control for Aerial Refueling Autopilot

Linear Quadratic Regulator （LQR） is modem linear control that is suitable for multivariable state feedback and is known to yield good performance for linear systems or for nonlinear systems where the nonlinear aspects are presented. The fuzzy control is known to have the ability to deal with nonlinearities without having to use advanced mathematics. The LQR integrated fuzzy control （LQRIFC） simultaneously makes use of the good performance of LQR in the region close to switching curve, and the effectiveness of fuzzy control in region away from switching curve. A new analysis of the fuzzy system behavior presented helps to make possible precise integration of LQR features into fuzzy control. The LQRIFC is verified by simulation to suppress the uncertainty instability more effectively than the LQR besides minimizing the time of the mission proposed.

Aerial refueling scheduling of multi-receiver and multi-tanker under spatial-temporal constraints for forest firefighting

Forest fires pose a significant threat to human life and property,so the utilization of unmanned aircraft systems provides new ways for forest firefighting.Given the constrained load capacities of these aircraft,aerial refueling becomes crucial to extend their operational time and range.In order to address the complexities of firefighting missions involving multi-receiver and multi-tanker deployed from various airports,first,a fuel consumption calculation model for aerial refueling scheduling is established based on the receiver path.Then,two distinct methods,including an integrated one and a decomposed one,are designed to address the challenges of establishing refueling airspace and allocating tasks for tankers.Both methods aim to optimize total fuel consumption of the receivers and tankers within the aerial refueling scheduling framework.The optimization problem is established as nonlinear optimization models along with restrictions.The integrated method seamlessly combines refueling rendezvous point scheduling and tanker task allocation into unified process.It has a complete solution space and excels in optimizing total fuel consumption.The decomposed method,through the separation of rendezvous point scheduling and task allocation,achieves a reduced computational complexity.However,this comes at the cost of sacrificing optimality by excluding specific feasible solutions.Finally,numerical simulations are carried out to verify the feasibility and effectiveness of the proposed methods.These simulations yield insights crucial for the practical engineering application of both the integrated and decomposed methods in real-world scenarios.This comprehensive approach aims to enhance the efficiency of forest firefighting operations,mitigating the risks posed by forest fires to human life and property.

Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poor accuracy.Recent studies reveal that optimization-based methods provide accurate and quick solutions for saliency detection.This paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue region.It can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and robustness.Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues.The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

Progress in modeling and control of probe-and-drogue autonomous aerial refueling

By refueling aircraft while they are in flight,aerial refueling is an efficient technique to extend their endurance and range.Autonomous Aerial Refueling(AAR)is anticipated to be used to complete aerial refueling for unmanned aircraft.There are three aerial refueling methods:the Probe-and-Drogue Refueling(PDR)refueling method,the flying-boom refueling method,and the boom-drogue-adapter refueling method.The paper considers the PDR approach,the most challenging of the three,because the flexible hose-drogue assembly has fast dynamics and is susceptible to various kinds of winds,which makes the probe docking with the drogue difficult.PDR is divided into four phases,namely the rendezvous phase,joining phase,refueling phase,and reform phase,with the refueling phase being the most crucial.The controller design faces the greatest challenge during the docking control of the refueling phase since it calls for a high level of safety,precision,and efficiency.As a result,the modeling and control issues encountered during the refueling phase are typical and difficult.The fundamental idea of AAR is presented in the paper first,after which the characteristics and requirements of AAR are outlined.The progress in modeling and control techniques for the AAR’s refueling phase is then systematically reviewed.Finally,potential future work for high safety,precision,and efficiency requirements is examined and suggested.

Dynamic modeling of a hose-drogue aerial refueling system and integral sliding mode backstepping control for the hose whipping phenomenon

Dynamic modeling of a hose-drogue aerial refueling system（HDARS） and an integral sliding mode backstepping controller design for the hose whipping phenomenon（HWP） during probe-drogue coupling are studied. Firstly, a dynamic model of the variable-length hose-drogue assembly is built for the sake of exploiting suppression methods for the whipping phenomenon.Based on the lumped parameter method, the hose is modeled by a series of variable-length links connected with frictionless joints. A set of iterative equations of the hose＇s three-dimensional motion is derived subject to hose reeling in/out, tanker motion, gravity, and aerodynamic loads accounting for the effects of steady wind, atmospheric turbulence, and tanker wake. Secondly,relying on a permanent magnet synchronous motor and high-precision position sensors, a new active control strategy for the HWP on the basis of the relative position between the tanker and the receiver is proposed. Considering the strict-feedback configuration of the permanent magnet synchronous motor, a rotor position control law based on the backstepping method is designed to insure global stability. An integral of the rotor position error and an exponential sliding mode reaching law of the current errors are applied to enhance control accuracy and robustness. Finally,the simulation results show the effectiveness of the proposed model and control laws.

Vision-based Estimation of Relative Pose in Autonomous Aerial Refueling

The lack of autonomous aerial refueling capabilities is one of the greatest limitations of unmanned aerial vehicles. This paper discusses the vision-based estimation of the relative pose of a tanker and unmanned aerial vehicle, which is a key issue in autonomous aerial refueling. The main task of this paper is to study the relative pose estimation for a tanker and unmanned aerial vehicle in the phase of commencing refueling and during refueling. The employed algorithm includes the initialization of the orientation parameters and an orthogonal iteration algorithm to estimate the optimal solution of rotation matrix and translation vector. In simulation experiments, because of the small variation in the rotation angle in aerial refueling, the method in which the initial rotation matrix is the identity matrix is found to be the most stable and accurate among methods. Finally, the paper discusses the effects of the number and configuration of feature points on the accuracy of the estimation results when using this method.

Real-time drogue recognition and 3D locating for UAV autonomous aerial refueling based on monocular machine vision

Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.

Modeling and simulation of bow wave effect in probe and drogue aerial refueling

In a probe and drogue aerial refueling system, the bow wave of the receiver aircraft will produce a strong aerodynamic effect on the drogue once the receiver follows the drogue at a close distance. It is a major difficulty of docking control in the probe and drogue refueling. This paper analyses the bow wave effect and presents a simple method to model it. Firstly, the inviscid flow around the receiver is modeled based on the stream function defined by basic stream singularities. Secondly, a correction function is developed to eliminate the error caused by the absence of air vis- cosity. Then, the aerodynamic coefficients are used to calculate the induced aerodynamic force on the drogue. The obtained model is in an analytical form that can be easily applied to the controller design and the real-time simulations. In the verification part, computational fluid dynamics （CFD） simulation tests are conducted to validate the obtained flow fields and aerodynamic forces. Finally, the modeling method is applied to an F-16 receiver aircraft in a previously developed autonomous aerial refueling simulation system. The simulations results are analyzed and compared with the NASA flight-test data, which demonstrates the effectiveness of the proposed method.

Drogue detection for autonomous aerial refueling based on convolutional neural networks

Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling（AAR）. To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks（CNNs） is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue＂ aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units（GPUs）, a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method＇s accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability.

Modeling and simulation of a time-varying inertia aircraft in aerial refueling

Studied in this paper is dynamic modeling and simulation application of the receiver aircraft with the time-varying mass and inertia property in an integrated simulation environment which includes two other significant factors, i.e., a hose-drogue assembly dynamic model with the variable-length property and the wind effect due to the tanker＇s trailing vortices. By extending equations of motion of a fixed weight aircraft derived by Lewis et al., a new set of equations of motion for a receiver in aerial refueling is derived. The equations include the time-varying mass and inertia property due to fuel transfer and the fuel consumption by engines, and the fuel tanks have a rectangle shape rather than a mass point. They are derived in terms of the translational and rotational position and velocity of the receiver with respect to an inertial reference frame. A linear quadratic regulator （LQR） controller is designed based on a group of linearized equations under the initial receiver mass condition. The equations of motion of the receiver with a LQR con- troller are implemented in the integrated simulation environment for autonomous approaching and station-keeping of the receiver in simulations.

A guidance law for UAV autonomous aerial refueling based on the iterative computation method

The rendezvous and formation problem is a significant part for the unmanned aerial vehicle（UAV） autonomous aerial refueling（AAR） technique. It can be divided into two major phases： the long-range guidance phase and the formation phase. In this paper, an iterative computation guidance law（ICGL） is proposed to compute a series of state variables to get the solution of a control variable for a UAV conducting rendezvous with a tanker in AAR. The proposed method can make the control variable converge to zero when the tanker and the UAV receiver come to a formation flight eventually. For the long-range guidance phase, the ICGL divides it into two sub-phases： the correction sub-phase and the guidance sub-phase. The two sub-phases share the same iterative process. As for the formation phase, a velocity coordinate system is created by which control accelerations are designed to make the speed of the UAV consistent with that of the tanker.The simulation results demonstrate that the proposed ICGL is effective and robust against wind disturbance.

Pilot multi-axis control behavior modeling of receivers in probe-and-drogue aerial refueling

The probe-and-drogue aerial refueling(PDR)task is seriously disturbed by the wind field,so it is difficult to obtain accurate visual information of the probe and drogue position.Additionally,the pilot needs to control the vertical,lateral and forwardbackward positions of the receiver through the stick and throttle lever,and the pitch and roll manipulation actions of the stick should be reasonably distributed.To simulate the behavior of the pilot’s visual perception and the stick manipulation allocation in the PDR task,three modules are designed,including the visual information acquisition module(VIAM),strategy switching module(SSM)and pilot control and action module(CAM).Based on the coherence function analysis of the flight simulation test data,it is proven that the pilot’s multi-axis control behavior can be decoupled,and the flight states to be controlled are determined.Finally,the established multi-axis receiver pilot model(EPM)is developed.Combined with the motion models of the receiver,the tanker and the refueling equipment,a PDR flight task simulation model is established,and the evaluation indicators for the receiver’s flight performance during the PDR task,such as capture time and settling time,are proposed.The PDR flight simulation model was used to build the ground-based flight simulation test platform,and the flight simulation test of the PDR task was carried out.A comparison between the flight simulation test data and the numerical simulation results shows that the calculation error is less than 10%,which verifies the correctness of the established pilot model.The pilot model can be used to evaluate the design scheme of the refueling equipment and the receiver flight control law and provide a theoretical reference for the flight test design of the PDR task.

A monocular visual measurement system for UAV probe-and-drogue autonomous aerial refueling

Purpose–The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling(AAR)for unmanned aerial vehicle,which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approach–Methods and techniques for marker detection,feature matching and pose estimation have been designed and implemented in the visual measurement system.Findings–The simple blob detection(SBD)method is adopted,which outperforms the Laplacian of Gaussian method.And a novel noise-elimination algorithm is proposed for excluding the noise points.Besides,a novel feature matching algorithm based on perspective transformation is proposed.Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implications–The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/value–The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed.Besides,a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently,deep learning has been widely utilized for object tracking tasks.However,deep learning encounters limits in tasks such as Autonomous Aerial Refueling(AAR),where the target object can vary substantially in size,requiring high-precision real-time performance in embedded systems.This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem.First,an Asymmetric Convolutional Network(ACNet)and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background.The prior object information,such as its location in the previous frame and its speed,is utilized to adaptively track objects of various sizes.Moreover,based on the n-fold Bernoulli probability theorem,we develop a filter that uses statistical laws to reduce the false positive rate of object tracking.To evaluate the efficiency of our algorithm,a new AAR dataset is collected,and extensive AAR detection and tracking experiments are performed.The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks;the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.