Aircraft State Research Articles

A fault tolerant control for iced aircraft based on fixed-time incremental dynamic inversion with FTESO

Icing seriously affects the aerodynamic coefficients and flight performance of an aircraft. To address this problem, in this paper a fixed-time incremental nonlinear dynamic inversion(INDI) control method based on a fixed-time dilated state observer(FTESO) is proposed, and a flight controller for a certain type of transport aircraft is designed based on this method, which is used to enhance the flight control capability under icing conditions, and thus improve the flight safety. In this designed controller, the FTESO is used to estimate the disturbance in the angular acceleration signal, which solves the problem that the robustness of the existing INDI control depends on the accuracy of the angular acceleration. Moreover, the control method can ensure that the aircraft state errors converge to a stable region in a fixed time, and the convergence time does not depend on initial errors. Simulation results show that the designed controller is able to overcome the effect of icing on the aircraft and ensure that the aircraft achieves the desired flight performance even in angular acceleration disturbances.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

Aircraft Takeoff and Landing Weight Estimation from Surveillance Data

Aircraft weight estimation is a common problem facing researchers working with aircraft surveillance data. Although knowledge of an aircraft’s weight and thrust is required for many types of analyses, such as those evaluating aircraft acoustic noise, fuel burn, and emissions, these parameters are typically not available from surveillance sources. Instead, researchers generally only have access to basic aircraft states: lateral position, groundspeed, and altitude. Therefore, methods for estimating the weight of aircraft from these basic states become necessary in cases where aircraft performance is a key component of the analysis. This paper introduces two weight estimation models: one for the estimation of aircraft takeoff weight from departure data, and another for the estimation of aircraft landing weight from arrival data. The models are mathematically simple but grounded in knowledge of aircraft certification, airline operations, and aircraft flight management system logic. The landing weight estimation model proposed is shown to have a mean absolute error equivalent to 2.66% of maximum takeoff weight and a standard deviation of 3.35% of maximum takeoff weight when validated using onboard data recordings from 240 Airbus A320 flights. Similarly, the proposed takeoff weight estimation model is shown to have a mean absolute error of 2.83% of the maximum takeoff weight and a standard deviation of 3.55% of the maximum takeoff weight when applied to the same validation dataset.

Design and simulation of system to prevent runway overrun during landing of civil aircraft

Abstract The airborne runway overrun prevention system predicts landing distance by utilizing real-time aircraft status parameters obtained through onboard sensors. This system offers early warnings to pilots, thereby enhancing flight safety. This paper introduces a design scheme for a runway overrun prevention system considering aircraft dynamic performance. Initially, the study analyzes the basic components and functional requirements of the system based on the Minimum Operational Performance Standard For A Runway Overrun Awareness And Alerting System by the European Aviation Safety Agency. Subsequently, the approach and landing phases are segmented into four stages, providing a methodology for calculating landing distance using a dynamic model. Finally, concluded with a simulation verification conducted on the X-Plane flight simulation platform, we test the visual alarm system of the proposed runway overrun prevention system. Simulation results demonstrate that, during the landing phase, the system dynamically calculates and updates stopping points for each braking mode based on the current braking method. In situations where there is a risk of runway overrun, the system issues timely and well-founded alarms.

Adaptive dynamic programming base on MMC device of a flexible high-altitude long endurance aircraft

Flexible high-altitude long endurance (HALE) aircraft face challenges such as the delay effect caused by aeroelasticity and low efficiency during high-altitude cruising. This paper addresses a new active control scheme that replaces traditional aerodynamic control surfaces with longitudinal and lateral moving masses and adopts an observer-based adaptive dynamic programming (ADP) control strategy, aiming to solve the attitude control of HALE aircraft with flexible wings. Firstly, considering the account of unsteady aerodynamics, attitude angular velocity, and moving masses, complete dynamic model is established for a flexible aircraft, in order to more accurately describe the state of the HALE aircraft. This model includes two moving masses to replace traditional ailerons and elevators. It is difficult to design attitude control strategies considering the delay effects and unknown dynamic disturbances of HALE aircraft. To this end, the actor-critic structure of the ADP approach is designed to achieve the near-optimal control strategy of the system, which eliminates the need for prior knowledge of model parameters. A fixed time disturbance observer (FTDO) is proposed to estimate the future tracking error information and unknown disturbances to improve the control performance of the attitude. Finally, the stability of the system is proved via the Lyapunov technique and a comprehensive simulation of HALE aircraft is provided. The simulation results show that the proposed control algorithm can enable the aircraft attitude angles to quickly response and maintain stability under gust wind. Compared with a single ADP control strategy, this paper's algorithm has advantages in response speed and error.

Design and Implementation of Simulation Training System for Ground Maintenance Pannel of a Certain Type of Aircraft

According to the design requirements of ground maintenance pannel simulation training system of a certain type of aircraft, a set of training system with the functions of ground handling practice, aircraft maintenance operation training and PMA interactive operation training is developed. The system design fully considers the requirements of generalization and modularization design. The function division of each module is clear, the signal interface is clear, and one of the modules can be replaced quickly to facilitate the positioning and elimination of failure. It has the dual expansibility of training task and training model, and can continuously add training task package, including procedure training, troubleshooting guidance training, etc., and can adapt to change in aircraft state.

Investigation of the Internal Flow Characteristics of a Tiltrotor Aircraft Engine Inlet in a Gust Environment

In the vertical take-off and landing (VTOL) state of tiltrotor aircraft, the inlet entrance encounters the incoming airflow at a 90° attack angle, resulting in highly complex internal flow characteristics that are extremely susceptible to gusts. Meanwhile, the flow quality at the inlet exit directly affects the performance of the aircraft’s engine. This work made use of an unsteady numerical simulation method based on sliding meshes to investigate the internal flow characteristics of the inlet during the hover state of a typical tiltrotor aircraft and the effects of head-on gusts on the inlet’s aerodynamic characteristics. The results show that during the hover state, the tiltrotor aircraft inlet features three pairs of transverse vortices and one streamwise vortex at the aerodynamic interface plane (AIP). The transverse vortices generated due to the rotational motion of the air have the largest scale and exert the strongest influence on the inlet’s performance, which is characterized by pronounced unsteady features. Additionally, strong unsteady characteristics are present within the inlet. Head-on gusts mainly affect the mechanical energy and non-uniformity of the air sucked into the inlet by influencing the direction of the rotor’s induced slipstream, thereby impacting the performance of the inlet. The larger head-on gusts have beneficial effects on the performance of the inlet. When the gust velocity reaches 12 m/s, there is a 1.01% increase in the total pressure recovery (σ) of the inlet, a 25.72% decrease in the circumferential distortion index (DC60), and a reduction of 62.84% in the area where the swirl angle |α| exceeds 15°. Conversely, when the gust velocity of head-on gusts reaches 12 m/s in the opposite direction, the inlet’s total pressure recovery decreases by 1.13%, the circumferential distortion index increases by 14.57%, and the area where the swirl angle exceeds 15° increases by 69.59%, adversely affecting the performance of the inlet. Additionally, the presence of gusts alters the unsteady characteristics within the inlet.

Research on Complementary Filtered Attitude Solution Method for Quadcopter Based on Double Filter Preprocessing

In the attitude calculation process of quadrotor, the traditional pre-filtering method has degraded the attitude solving accuracy due to incomplete denoising of accelerometer and gyroscope measurements. Therefore, a complementary filtered attitude solution method based on double filter pre-processing is proposed to address this problem. First, the signal decomposition, hard threshold denoising, and signal reconstruction of the accelerometer and gyroscope acquired data are performed using the Haar real-time wavelet filtering algorithm. The reconstructed signal is then filtered by the infinite impulse response ( IIR ) low-pass filtering algorithm to remove the residual high-frequency noise and complete the dual filtering preprocessing. Next, the gyroscope data is corrected with accelerometer data according to the complementary filtering algorithm. Finally, the corrected data are used to solve the quaternion and thus the attitude angle by combining the Longacurta method. The results show that the dual filtering preprocessing method can further reduce the noise in the measurements of accelerometer and gyroscope. The attitude angle results calculated by the proposed method in the static and dynamic hovering states of the four-rotor aircraft have a small degree of dispersion, which can effectively improve the accuracy of attitude calculation.

Resource-constrained project scheduling with multiple states: Bi-objective optimization model and case study of aircraft maintenance

This paper introduces a new resource-constrained project scheduling problem in which the project has multiple possible states and whether an activity can be performed at a certain time depends on the state at that time. We are motivated by aircraft maintenance projects where activities need to be performed in particular aircraft states, such as power on/off and jack on/off. We formulate a bi-objective optimization model for this problem, which determines the schedule of activities and the selection of states over the planning horizon, subject to resource constraints, precedence relationship, and state restrictions. The model concerns both the minimization of the project duration and the minimization of the number of state changes over time. A heuristic algorithm, which utilizes genetic algorithm techniques, is developed to obtain Pareto efficient solutions of the model. Its effectiveness is demonstrated through a comparison with CPLEX and an adaptation of NSGA-II. Furthermore, we conduct a case study about aircraft maintenance C-check projects in a major airline. The case study demonstrates that our optimization-based approach is applicable and beneficial to the project scheduling practice with the consideration of states.

Safe and Scalable Real-Time Trajectory Planning Framework for Urban Air Mobility

This paper presents a real-time trajectory planning framework for urban air mobility (UAM) that is both safe and scalable. The proposed framework employs a decentralized, free-flight concept of operation in which each aircraft independently performs separation assurance and conflict resolution, generating safe trajectories by accounting for the future states of nearby aircraft. The framework consists of two main components: a data-driven reachability analysis tool and an efficient Markov-decision-process-based decision maker. The reachability analysis overapproximates the reachable set of each aircraft through a discrepancy function learned online from simulated trajectories. The decision maker, on the other hand, uses a 6-degree-of-freedom guidance model of fixed-wing aircraft to ensure collision-free trajectory planning. Additionally, the proposed framework incorporates reward shaping and action shielding techniques to enhance safety performance. The proposed framework was evaluated through simulation experiments involving up to 32 aircraft in a generic city-scale area with a 15 km radius, with performance measured by the number of near-midair collisions (NMAC) and computational time. The results demonstrate the planner’s ability to generate safe trajectories for the aircraft in polynomial time, showing its scalability. Moreover, the action shielding and reward shaping strategies show up to a 78.71 and 85.14% reduction in NMAC compared to the baseline planner, respectively.

Autonomous predictive maintenance of quadrotor UAV with multi-actuator degradation

AbstractWith the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

Acoustic emission diagnostics of a hull structures by a system of integrating fiber-optic sensors for the aircraft and spacecraft safe operation

A variant of aircraft and spacecraft state of hull structures acoustic emission diagnostics method based on a system of distributed fiber-optic sensors is considered. Such systems can be sources of information for a technical means of ensuring safety at aerospace facilities. The Green's function for a single sensor is found, and the features of its operation under the influence of pulsed acoustic emission signals are considered. The capabilities of the sensor system under consideration in estimating the coordinates and parameters of acoustic emission signals are determined. The results of experimental studies demonstrating the possibility of detecting emission signals under conditions of specific interference are reported.

Aviation Safety Risk Analysis Based on Bayesian Network Modeling

The occurrence of serious flight accidents not only brings huge economic losses to airlines but also poses a great threat to passengers' lives. The occurrence of serious flight accidents will not only bring great economic losses to airlines but also cause great threats to the lives of passengers. Therefore, in this paper, based on the flight records of different flight crews, flight routes, airports, and specific flight conditions, aircraft safety risk analysis is conducted to calculate and evaluate the risk propensity. Firstly, the data are preprocessed, and then the QAR data are clustered to extract the key data items affecting the safe flight of the aircraft; then the data are transformed to the attached data, and a hierarchical clustering model is established based on the K-means clustering algorithm, which classifies the factors affecting the safety quality of the aircraft flight into the three main aspects of environmental factors, crew maneuvering, and aircraft state, and the key parameters are weighted by the Random Forest algorithm. weights of the key parameters. Then, based on the 3 criterion, the outliers exceeding 3 were selected as the dependent variables leading to the occurrence of deviation; based on the three-layer model, a Bayesian neural network model based on the three-layer model, and then determine the Bayesian nodes; finally, calculate the Bayesian node probability, and then provide a reasonable quantitative description of the flight maneuver.

Training Sample Pattern Optimization Based on a Swarm Intelligence Algorithm for Tiltrotor Flight Dynamics Model Approximation

Neural networks have been widely used as compensational models for aircraft control designs and as surrogate models for other optimizations. In the case of tiltrotor aircraft, the total number of aircraft states and controls is much greater than that of both traditional fixed-wings and helicopters. This requires, in general, a huge amount of training data for the network to reach a satisfactory approximation precision and makes the network size rise considerably. To solve the practical problem of reducing the size of the approximating network, efforts have to be made in the efficient utilization of the limited amount of training data. This work presents the methodology of optimizing the sample pattern of the training data set by adopting the metaheuristic algorithm of the particle swarm optimizer improved by the fourth-order Runge–Kutta algorithm. A 6-degree-of-freedom nonlinear flight dynamics model of the tiltrotor aircraft is derived, along with its approximation radial basis function neural network. An example case of approximating a highly nonlinear function is studied to illustrate the principle and main parameters of the optimizer, and the approximation performance of the time-domain response of the unstable nonlinear system is revealed by the study of a Van der Pol oscillator. Then, the presented method is applied to the modeled tiltrotor aircraft for its early and late conversion modes. The optimization scheme shows great improvement in both cases, as the function approximation error is reduced significantly.

Automatic landing of carrier-based aircraft based on a collaboration of fault reconstruction and fault-tolerant control

Considering the actuator faults that occur during the automatic landing of carrier-based aircraft, a novel automatic landing method based on a collaboration of fault reconstruction and fault-tolerant control is proposed. The variables of the aircraft states and actuator fault are combined as a new state, and an extended state observer (ESO) is constructed to establish the deviation-based observation equation. A fault-tolerant control law based on state feedback is designed and a closed-loop control system of carrier-based aircraft landing is established. A new collaborative control algorithm is proposed based on the extended state observer and fault-tolerant control law, which solves fault reconstruction with unknown parameters and fault-tolerant control simultaneously, so that the fault estimation and control with actuator fault are decoupled. The linear matrix inequality (LMI) is utilized to complete the specific calculation. The effectiveness of the proposed collaborative algorithm is verified through the different simulations with other compared algorithms under the condition of airwake disturbance and sensor noise. The simulation curves and experiment data further demonstrate the proposed algorithm exhibits better fault estimation and automatic landing control performance.

Poster] Aircraft Detection and State Estimation in Satellite Images

Unidentified flying objects are aircraft that do not continuously broadcast ADS-B. They pose a risk to air traffic safety. In this study, we introduce a method for detecting and estimating the state of aircraft in Sentinel-2 multispectral satellite images. We construct a dataset of 579 ADS-B annotated aircraft from 69 Sentinel-2 images. A CNN is trained on the dataset to aircraft state vector i.e. position, velocity, heading, and altitude. This work allows real-time monitoring of flying objects in satellite images.

Comparison of Adaptive Kalman Filters in Aircraft State Estimation

Aircraft state estimation refers to the process of determining the current or future state of an aircraft, such as its position, velocity, orientation, and other relevant parameters, based on available sensor data and mathematical models. This information is crucial for safe and efficient flight operations, as well as for various applications, including Guidance, Navigation, Control (GNC), and autonomous flight. Given the beginning circumstances, the motion of the airplane was examined in this study by estimating the state vectors using the Kalman Filter (KF) and the Adaptive Kalman Filters (AKF), as well as by comparing the various estimate techniques.

Достаточные условия существования Н∝ -наблюдателя состояния линейных непрерывных динамических систем

<p>The article deals with the problem of finding the observer of the state vector of linear continuous non-stationary dynamical systems with uncertainty of the initial conditions, limited external influences and measurement errors over a finite time interval. Sufficient conditions for the existence of an observer are formulated and proved on the basis of the expansion principle. Relationships are obtained for finding the parameters of the observer and the worst laws of change in external influences and measurement errors. As a limiting case, the problem of observer synthesis for stationary linear dynamical systems on a semi-infinite time interval is considered. Two applied problems of estimating the aircraft state vector based on the results of incomplete and inaccurate measurements are solved.</p>

Autonomous Remote Control of Small Unmanned Aircraft

In this paper we present a novel approach to controlling small unmanned aircraft in third-person perspective scenarios. Our approach uses monocular imagery to mimic the human ability to perceive and control a remote aircraft. This allows for the autonomous control of a wide variety of aircraft with minimal on-board sensors. We improve upon other 2D single-shot detection methods by estimating a 3D box containing the aircraft, which allows us to capture a vehicle’s position and attitude as measurements. Using a constant-jerk Kalman filter, we then estimate the complete aircraft state from these measurements. Finally a proportional-integral-derivative controller is used to provide closed-loop autonomous flight of an aircraft from a remote location. Hardware experiments of a completely autonomous vehicle flying waypoints illustrate the validity of this method.

Geofencing for Optionally Piloted Aircraft Through Automatic Execution of Smooth Evasive Maneuvers

The paper proposes a method for generating and automatically executing smooth evasive maneuvers which allow to enforce a geofence for an optionally piloted fixed-wing aircraft. Feasible trajectories are planned online based on the current aircraft state and performance limitations, including finite roll rates. Collision checks are continuously carried out in the background to take over control from the pilot before violating the geofence becomes inevitable. The system automatically executes an evasive maneuver before giving back control to the pilot. The approach can readily be applied to the general collision avoidance problem for convex obstacles. We validate the feasibility and geofencing accuracy of the proposed framework by showcasing results of flight tests of an unmanned, fixed-wing aircraft.