Experimental Flight Test Research Articles

Experimental evaluation of wind turbine wake turbulence impacts on a general aviation aircraft

Abstract. Continued development of wind farms near populated areas has led to rising concerns about the potential risk posed to general aviation aircraft when flying through wind turbine wakes. There is an absence of experimental flight test data available with which to assess this potential risk. This paper presents the results of an instrumented flight experiment in which a general aviation aircraft was flown through the wake of a utility-scale wind turbine at an operating wind farm. Wake passes were flown at different downwind distances from the turbine, and data were collected on the orientation disturbances, altitude and speed deviations, and acceleration loads experienced by the aircraft. Videos and pilot statements were also collected, providing qualitative information about the disturbances encountered in the wake. Results show that flight disturbances were small in all cases, with no difference observed between flight data inside and outside the wake at distances greater than six rotor diameters from the turbine. At distances closer than six rotor diameters, small load factor and orientation disturbances were noted but were commensurate with those experienced in light or moderate atmospheric turbulence. Overall, the loads and disturbances experienced were far smaller than those that would risk causing loss of control or structural damage.

A switched adaptive strategy for state estimation in MEMS-based inertial navigation systems with application for a flight test

Abstract This paper proposes a switched model to improve the estimation of Euler angles and decrease the inertial navigation system (INS) error, when the centrifugal acceleration occurs. Depending on the situation, one of the subsystems of the proposed switched model is activated for the estimation procedure. During global positioning system (GPS) outages, an extended Kalman filter (EKF) operates in the prediction mode and corrects the INS information, based on the system error model. Compared with previous works, the main advantages of the proposed switched-based adaptive EKF (SAEKF) method are (i) elimination of INS error, during the centrifugal acceleration, and (ii) high accuracy in estimating the attitude and positioning, particularly during GPS outages. To validate the efficiency of the proposed method in various trajectories, an experimental flight test is performed and discussed, involving a microelectromechanical (MEMS)-based INS. The comparative study shows that the proposed method considerably improves the accuracy in various scenarios.

Design and real-time implementation of a robust backstepping non-singular integral terminal sliding mode attitude control for a quadrotor aircraft

This paper concerns the problem of robust attitude control of a quadrotor aircraft subjected to modeling inaccuracies and wind disturbances. First, the model dynamics and state space representation of the quadrotor system are designed by considering the impact of disturbances and uncertainties. Then, a novel robust backstepping non-singular integral terminal sliding mode control (BNITSMC) is synthesized for the quadrotor attitude with the aim of enhancing the tracking accuracy. To strengthen the system’s robustness and guarantee the reachability of the sliding surface while reducing chattering, the supertwisting algorithm (ST) is used. Closed-loop stability and state convergence are guaranteed via the Lyapunov theory. Some experimental flight tests have been executed to validate the workability of the developed flight control. The obtained results prove that the recommended technique can provide improved performance in terms of stabilization and tracking precision.

An audio‐based risky flight detection framework for quadrotors

AbstractDrones have increasingly collaborated with human workers in some workspaces, such as warehouses. The failure of a drone flight may bring potential risks to human beings' life safety during some aerial tasks. One of the most common flight failures is triggered by damaged propellers. To quickly detect physical damage to propellers, recognise risky flights, and provide early warnings to surrounding human workers, a new and comprehensive fault diagnosis framework is presented that uses only the audio caused by propeller rotation without accessing any flight data. The diagnosis framework includes three components: leverage convolutional neural networks, transfer learning, and Bayesian optimisation. Particularly, the audio signal from an actual flight is collected and transferred into time–frequency spectrograms. First, a convolutional neural network‐based diagnosis model that utilises these spectrograms is developed to identify whether there is any broken propeller involved in a specific drone flight. Additionally, the authors employ Monte Carlo dropout sampling to obtain the inconsistency of diagnostic results and compute the mean probability score vector's entropy (uncertainty) as another factor to diagnose the drone flight. Next, to reduce data dependence on different drone types, the convolutional neural network‐based diagnosis model is further augmented by transfer learning. That is, the knowledge of a well‐trained diagnosis model is refined by using a small set of data from a different drone. The modified diagnosis model has the ability to detect the broken propeller of the second drone. Thirdly, to reduce the hyperparameters' tuning efforts and reinforce the robustness of the network, Bayesian optimisation takes advantage of the observed diagnosis model performances to construct a Gaussian process model that allows the acquisition function to choose the optimal network hyperparameters. The proposed diagnosis framework is validated via real experimental flight tests and has a reasonably high diagnosis accuracy.

Eagle-inspired manipulator with adaptive grasping and collapsible mechanism and modular DOF for UAV operations

This paper presents an eagle-inspired manipulator designed to enhance the efficiency of medium and large multi-rotor UAVs during contact operations such as picking, obstacle cleaning, and grasping. Drawing inspiration from the skeleton structure and motion law of an eagle’s leg, we propose a prototype design of a large load, wide range of movement, multiple DOF, and collapsible manipulator. The main structure of the manipulator consists of a planetary reducer and a coupled parallel linkage mechanism, which enables it to have greater payload capacity while also increasing its range of motion. The grasping mechanism is based on the motion law of an eagle’s claw, which effectively increases the contact area with targets. To further enhance the DOF of the grasping mechanism, we have designed an ankle mechanism that utilizes a pair of double helical telescopic mechanisms to constrain the DOF of the universal joint, connecting the grasping mechanism to the main structure of the manipulator. Regarding the comparison with existing techniques, our eagle-inspired manipulator has several advantages over previous designs. On one hand, the grasp planning strategy, which is inspired by the motion law of an eagle’s claw, can achieve more reliable and accurate grasping than existing techniques. On the other hand, the manipulator’s collapsible design facilitates easier transportation and storage while maintaining its wide range of motion. What’s more, the modular DOF design allows for greater flexibility in adapting to different payloads and operating environments when compared to existing technologies. We outline the development process of this manipulator, including kinematic modeling and control. The ability of the manipulator system is tested, and flight test experiments are conducted on a UAV carrying the manipulator to demonstrate its practicality and effectiveness.

Go with the Flow: Estimating Wind Using Uncrewed Aircraft

This paper presents a fundamentally different approach to wind estimation using Uncrewed Aircraft (UA) than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces. Using only GPS and orientation sensors, the strategy estimates wind vectors in an Earth-fixed frame during turning maneuvers. The method presented here is called the Wind-Arc method. The philosophy behind this method has been seen in practice, but this paper presents an alternative derivation with resulting performance evaluations in simulations and flight tests. The simulations verify the method provides perfect performance under ideal conditions using simulated GPS, heading angle, and satisfied assumptions. When applied to experimental flight test data, the method works and follows both the airspeed and wind speed trends, but improvements can still be made. Wind triangles are displayed at each instant in time along the flight path that illustrate the graphical nature of the approach and solution. Future work will include wind gust estimation and a Quality of Estimate (QoE) metric to determine what conditions provide good wind speed estimates while preserving the method’s generality and simplicity.

Real-Time Nonlinear Control Allocation Framework for Vehicles with Highly Nonlinear Effectors Subject to Saturation

Hybrid Unmanned Aerial Vehicles UAV are vehicles capable of take-off and landing vertically like helicopters while maintaining the long-range efficiency of fixed-wing aircraft. Unfortunately, due to their wing area, these vehicles are sensitive to wind gusts when hovering. One way to increase the hovering wind-rejection capabilities of hybrid UAV is through the addition of extra actuators capable of directing the thrust of the rotors. Nevertheless, the ability to control UAVs with many actuators is strictly related to how well the Control Allocation problem is solved. Generally, to reduce the problem complexity, conventional (CA) methods make use of linearized control effectiveness in order to optimize the inputs that achieve a certain control objective. We show that this simplification can lead to oscillations if it is applied to thrust vectoring vehicles, with pronounced non-linear actuator effectiveness. When large control objectives are requested or actuators saturate, the linearized effectiveness based CA methods tend to compute a solution far away from the initial actuator state, invalidating the linearization. A potential solution could be to impose limits on the solution domain of the linearized CA algorithm. However, this solution only reduces the oscillations at the expense of a lag in the vehicle acceleration response. To overcome this limitation, we present a fully nonlinear CA method, which uses an Sequential Quadratic Programming (SQP) algorithm to solve the CA problem. The method is tested and implemented on a single board computer that computes the actuator solution in real time onboard a dual axis tilting rotor quad-plane. Flight test experiments confirm the problem of severe oscillations in the linearized effectiveness CA algorithms and show how the only algorithm able to optimally solve the CA problem is the presented Nonlinear method.

Least global position information based control of fixed-wing UAVs formation flight: Flight tests and experimental validation

The paper addresses the design of an innovative navigation and control method for the flight control of a formation consisting of small and low-cost fixed-wing UAVs by using a leader-follower strategy and a least global position information sharing algorithm. No additional hardware is needed except the classical global positioning system and autopilot. The novel strategy firstly involves the obtaining of the relative kinematics for the formation of UAVs by using a trajectory coordinate system and a navigation algorithm based on global position coordinates. Secondly, the flight control laws for each UAV are designed by means of the Active Disturbance Rejection Control, the convergence and robustness are discussed and proved, while the flight control laws for the whole formation are derived within a PI controller which balances the robustness and the computation of the whole control architecture. Then, by considering the requirements of positioning accuracy in low-cost constrains, the control scheme is validated both by numerical simulations and experimental flight tests along a racetrack-shaped circular path. The results prove an excellent accuracy for the navigation and control method, the follower UAVs following the trajectory of the leader UAV no matter if the flight is straight or turning type.

Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend Random Tree

This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment.

Online parameter identification and optimal input design using perturbed nonlinear programming

Flight tests are performed to gather data for flight vehicle parameter estimation. If an a priori model is available, the inputs during the runtime of the flight test experiment can be designed, such that the information content in the gathered data is maximized. These optimal inputs, however, are based on an inaccurate system model, and the improvement of the system model is the ultimate goal of the system identification and therewith, the flight tests. There is an implicit relationship between the optimal inputs and the system model. In this paper, we investigate a scheme to update the optimal inputs during the flight using an online optimization technique, while performing online parameter estimation. An extended Kalman filter is used to compute new parameter values during the maneuver runtime. Concurrent to that, an interior point NLP solver computes the optimal maneuver updates, as new parameters become available in each iteration. The method is validated via a numerical example.

AB-INITIO PILOTS’ PERSPECTIVES ON THE USE OF SIMULATION IN THE AVIATION ENGLISH COURSE

Flying is costly, time-consuming, dependent on weather and maintenance, and sometimes simply does not match with schedules well. When flying a real aircraft is not a feasible or even preferable for one reason or another, a flight simulator could be remedy. Using a flight simulator to improve ab-initio pilots’ knowledge and abilities may make their flying experience more effective and enjoyable. In this respect, this study aims to enhance aviation training through simulation-based learning and develop an understanding of how prospective pilots perceive the innovative approach of simulation integration in Aviation English courses. The X-Plane version 11 was used in conjunction with an introductory course in Aviation English and provided a substitute for an actual flight test experience at an aeronautical university. The sampling consisted of 20 tertiary level students enrolled in the Aviation English course. After the intervention, randomly selected 7 students were interviewed to gain insights into their perceptions about the use of simulation.

Advances in Aircraft System Identification at NASA Langley Research Center

Advances in aircraft system identification at NASA Langley Research Center are discussed. The relevant time period includes the years since the last summary paper of this kind, which was published in the Journal of Aircraft in 2005. Research advances were achieved in flight test experiment design, frequency-domain modeling, real-time autonomous global modeling, rapid simulation development and updating, dynamic modeling in turbulence, flight data corrections, model uncertainty characterization, and aeroelastic modeling using distributed sensing. Possible future developments in the field are identified.

High-Fidelity Dynamics Modelling for the Design of a High-Altitude Supersonic Sounding Rocket

The accurate modelling and simulation of vehicle dynamics is a fundamental prerequisite for the design and experimental flight testing of aerospace vehicles. In the case of high-altitude supersonic sounding rockets, it is critically important to produce realistic trajectory predictions in a representative range of operational and environmental conditions as well as to produce reliable probability distributions of terminal locations. This article proposes a methodology to develop high-fidelity flight dynamics models that accurately capture aeroelastic, turbulence, atmospheric and other effects relevant to sounding rockets. The significance of establishing a high-fidelity model and of addressing such a problem in the context of developing a digital twin are discussed upfront, together with the key tools utilised in the analysis. In addition to state-of-the-art computational methods to determine the aerodynamic forces, moments and mass changes in various flight regimes (including parachute release), a detailed methodology for incorporating the dynamic aeroelastic response of the rocket is presented. The validity of the proposed method is demonstrated through a simulation case study, which utilises data from an existing rocket prototype. Results corroborate the correct implementation of the proposed algorithms and provide foundations for future research on virtual sensing and digital twin for autonomous navigation and guidance.

Global Navigation Satellite System Real-Time Kinematic Positioning Framework for Precise Operation of a Swarm of Moving Vehicles.

The global navigation satellite system (GNSS) real-time kinematic (RTK) technique is used to achieve relative positioning centimeter levels among multiple agents on the move. A typical GNSS RTK estimates the relative positions of multiple rover receivers with respect to a single-base receiver. In a fleet of rover GNSS receivers, this approach is inefficient because each rover receiver only uses GNSS measurements of its own and those sent from a single-base receiver. In this study, we propose a novel GNSS RTK framework that facilitates the precise positioning of a swarm of moving vehicles through the GNSS measurements of multiple receivers and broadcasts fixed-integer ambiguities of GNSS carrier phases. The proposed framework not only provides efficient RTK positioning but also reliable performance with a limited number of GNSS satellites in view. Our experimental flight tests with six GNSS receivers showed that the systematic procedure of the proposed framework could maintain lower than 6 cm of 3D RMS positioning errors, whereas the conventional RTK failed to resolve the correct integer ambiguities of double difference carrier phase measurements more than 13% in five out of nine total baselines.

Closed loop integration of air-to-air visual measurements for cooperative UAV navigation in GNSS challenging environments

This paper discusses the integration of air-to-air visual measurements within a cooperative multi-vehicle architecture conceived to improve navigation performance of small Unmanned Aerial Vehicles in GNSS-challenging environments. The key concept is to exploit cooperation with other aircraft flying under better GNSS coverage conditions by exchanging navigation data and using a monocular camera system for relative sensing purposes. Specifically, accurate line of sight, obtained using Deep Learning-based detectors and local image analysis, are complemented with distance information achieved through an innovative shape-based passive ranging approach accounting for both the target attitude and its position in the field of view. Camera-based measurements are combined with the estimates generated by the other onboard sensors, within a customized Extended Kalman Filter in a closed loop fashion, since navigation estimates are used in feedback as hints for visual processing. An experimental flight test campaign is carried out using two quadcopters. A comparison between filter performance achievable as a function of the specific set of available information sources, i.e., bearing-only vs. line of sight and ranging, is carried out. Results show that the trade-off between correct, false, and missed detections, as well as the passive ranging accuracy allow the filter ensuring metric-level positioning error within GNSS-challenging areas. The added value of using both bearing and range measurements strongly depends on the formation geometry and the GNSS coverage conditions and can be predicted thanks to the “generalized dilution of precision”.

Multilayer Low-Cost Sensor Local-Global Filtering Fusion Integrated Navigation of Small UAV

Aimed at improving the nonlinear integrated navigation solution performance of multiple low-cost sensors fusion, this paper presents a multilayer loosely-coupled, local-global, and step-optimized MF5DCKF (Multisensor Federated fifth-degree Cubature Kalman filter) state estimation algorithm for the small unmanned aerial vehicle (UAV). This method establishes a multilayer nonlinear integrated navigation model composed of the nonlinear attitude and heading reference system (AHRS) error model, strapdown inertial navigation system/global positioning system (SINS/GPS) error model, and strapdown inertial navigation system/barometer (SINS/BARO) error model to enhance the robustness and richness of the navigation module. Further, based on the above navigation models, a loosely-coupled error state fusion frame is designed to obtain the local convergent state vector. Simultaneously, a three-layer fifth-degree Cubature Kalman filter is proposed to improve the local state estimation accuracy. Subsequently, to optimize the estimated local state, this paper presents a novel distributed MF5DCKF scheme fusing the local state vector to calculate the global optimal state parameters in a step-optimized process. The experimental flight test results show that the proposed algorithm achieves a higher state solution accuracy and a better convergent performance compared with some conventional multisensor fusion algorithms. The new algorithm framework can provide applicability and reliability for the small UAV during the flight.

Design methods and manufacturing techniques applicable in constructing dynamically scaled airplane models

The article describes methods and techniques intended for designing and manufacturing scaled airplane models applicable in experimental flight testing. Reduced model with resized geometric dimensions should be constructed in such way to preserve similarity of its structural properties with properties of the real aircraft. Having kept the similarity of weight, stiffness and aerodynamic load distribution and maintaining properly scaled values of thrust-to-weight and wing loading ratios we can prepare scaled model showing in-flight features identical to the real airplane.A series of measuring and technological activities have to be undertaken to develop full CAD model mapping airframe geometry and structure of the real aircraft. The process chain includes measuring, design and technological activities. The development of the geometry model includes: scanning outer geometry of the real airframe, development of the surface geometry model in the CAD environment and then introducing inner geometry items which together with outer skin face make a completely assembled model of the aircraft. For the needs of constructing scaled model the full-size CAD model has to be reduced. The structure of that resized model do not have to be the same as the real one. Materials and structural elements can be quite different but after assembling them the similarity of static and dynamic structural effects should be proved. Within the manufacturing phase there are some necessary stages like preparing molds for laminating components, milling solid elements, assembly of structural components, surface finishing of the model-body surface. The final model was subjected to a load test in a special test stand to check its deformability under specific load. In the same way the numerical static analysis of the full-size FEM model was conducted. The distribution of static displacement results obtained in both cases were compared. The other way of checking structural properties of the scaled model is comparable analysis of resonant vibrations. However, in this case, vibration measurements were not carried out and model verification stopped on a comparative analysis of construction mass distribution.The procedure and results were elaborated on the example of the Tu-154M aircraft, of which the reduced model was developed in the Faculty of Mechatronics, Armament and Aerospace of the Military University of Technology (FMA MUT Warsaw, PL). Constructing and testing dynamically scaled model is one of many tasks solved in the framework of complex research project, of which the general purpose was to give numerical and experimental results useful in explaining the circumstances of the Smolensk disaster.

Design, development and experimental validation of a lightweight dual-arm aerial manipulator with a COG balancing mechanism

In this work, we present the design, fabrication, and experimental validation of a lightweight, low inertia dual-arm manipulator with a center of gravity (COG) balancing mechanism, specifically designed for aerial manipulation missions. The developed system, consisting of the dual-arm base and two arms with 6 degrees of freedom (DOFs) each, weighs 2.5 kg in total with a maximum payload of 1.0 kg per arm. The dual-arm system is designed such that it can be attached to different multirotors without making major design modifications. In addition, the design of the arms is conceived to decrease the inertia of the arms by utilizing a timing belt-based transmission mechanism. Moreover, the proposed dual-arm design employs prismatic joints to introduce the following distinctive features: 1) ability of each arm to dynamically adjust its COG for better flight performance; 2) fully independent control of each arm for performing different tasks simultaneously; 3) extended workspace and reach of the arms for enhancing operational capability and improving safety during aerial manipulation missions. Since the proposed design has low inertia and a compensation mechanism to deal with the disturbances caused by the COG change, the developed dual-arm system can be mounted on multirotors equipped with standard autopilots, opening the door for the widespread use of dual-arm manipulators with the commercially available UAV platforms. To ensure the robustness of the mechanical structure, an analysis using finite element methods (FEM) is conducted. Extensive experimental flight tests are performed to evaluate the proposed dual-arm design with a hexarotor equipped with a common off-the-shelf autopilot. The experimental results show that the influence of the arms motion over the hexarotor stability is minor in contactless flight due to the low weight and inertia of the proposed dual-arm design. Furthermore, better hovering performance is achieved by exploiting the ability of the proposed design to compensate the dual-arm COG displacement.

Experimental comparison of the effect of the number of redundant rotors on the fault tolerance performance for the proposed multilayer UAV

In this paper, experimental investigation of the flight performance of the proposed universal Unmanned Aerial Vehicle (UAV) in case of rotor failures is presented. In the experimental flight tests, proposed universal UAV that can be converted to many different configurations due to its non-standard multi-layer structure was used in two different configurations as a standard octorotor and a multi-layer dodecarotor. Multiple outdoor experiments are conducted to show flight safety and reliability of the proposed UAV in terms of rotor failure tolerance. In order to evaluate flight safety and reliability of the proposed UAV, take-off, hovering and landing flight performance analyzes were performed in cases where one or two motors/propellers were completely lost. As performance criteria, errors in the UAV’s roll, pitch and yaw angles, altitude error and vibration in three axes were determined. The flight test results are presented both numerically and graphically. According to the results, multi-layer dodecarotor type has shown a more stable flight performance in terms of angular position errors. In addition, in both types of UAVs, in the case of failure of two rotors rotating in the opposite direction, it has been observed that the error in yaw angle is less than a rotor failure, as expected. Likewise, in the case of failure of two rotors rotating in the same direction, performance loss was observed in the control of yaw angle.

In-Flight Estimation of Drag Parameters and Air Data for Unmanned Aircraft

Identifying the drag parameters of unmanned aerial vehicles (UAVs) is crucial for guaranteeing their aerodynamic efficiency. However, in contrast to commercial aircraft, obtaining accurate UAV drag parameters is challenging because the existing approaches rely on analytical models or require accurate modeling of the engine thrust, which highly depends on time-varying wind conditions. To address this challenge, this paper first proposes a novel in-flight estimation algorithm for the air data (airspeed, angle of attack, and sideslip angle) and drag parameters of UAVs. With this approach, there is no need to compute all of the contributing components for drag, to model the thrust of the UAVs, or to perform complicated wind tunnel testing/computational fluid dynamics analysis to obtain the drag parameter. Instead, the proposed algorithm requires only standard sensors such as inertial measurement units and air data systems during gliding flight. Then, an efficient glide phase detection algorithm for initiating the filter is proposed. Simulation and experimental flight testing of a UAV demonstrate that the proposed algorithm yields accurate zero lift drag coefficient, attitude, and air data estimation results according to thorough validation with newly derived metrics for performance evaluation.