Simulated Flight Data Research Articles

Nonlinear aeroelastic modeling of aircraft using support vector machine method

PurposeThis paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.Design/methodology/approachA system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression.FindingsThe results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks.Practical implicationsIn this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics.Originality/valueThis paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

Analytical Algorithm for Attitude and Heading Estimation Aided by Maneuver Classification

This paper presents a modified adaptive analytical algorithm for attitude and heading estimation. The analytical algorithm is based on the fusion of IMU, magnetometers and the velocity data from GPS. The kinematic Euler angles are first calculated based on the output of the rate gyros, then the calculated angle errors are compensated using the output of each of the accelerometers, magnetometers, and the velocity taken from a GPS receiver, without the need to model the systematic and random errors of the used sensors; Kalman filter is not used. The algorithm will be adaptive based on the maneuver classification, the filters’ parameters will be tuned depending on the maneuver intensity: No, Low, or High maneuver. The main contribution of this paper is to build an attitude and heading estimation algorithm (analytical algorithm) without using Kalman filter; this algorithm will be made adaptive based on the maneuver classification algorithm which was developed using logistic regression technique based on IMU output. Computer simulation with simulated and real flight data showed that the adaptive analytical algorithm has acceptable results compared to EKF.

Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.

MODEL REGRESI LINIER BAYESIAN DENGAN APLIKASI PADA DATA PENUNDAAN PENERBANGAN

Bayesian linear regression is an approach to linear regression where statistical analysis depend of Bayesian inference. The Bayesian model on big data uses a summary of data statistics as input; Statistical summary can be calculated from each subset, then a statistical summary of the full dataset is obtained from the sum of the summary statistics for each subset. Recent developments in data science and research, produce large datasets that are too large to be analyzed as a whole due to the limitations of computer memory or storage capacity. To overcome this, a program package was introduced from R namely BayesSummaryStatLM for the Bayesian linear regression model with the Markov Chain Monte Carlo implementation that overcomes this limitation. Then the program package from R, ff is used to read data in large datasets while calculating statistics summary. In this study Bayesian linear regression model used with several choices of prior distribution for unknown model parameters, and illustrates in simulation data and real datasets for flight delay data in US 2008. The application of simulation data and flight delay data produces a plot of density functions for the β parameters has a shape resembling a plot of Normal distribution density function, whereas for plot parameters the density function has a shape resembling the plot of Inverse Gamma distribution density function. In the simulation data, the estimator for each parameter produced has a value that approach to the value of the specified parameter (True Value). This is also indicated by the narrow credible interval for each parameters.

The Test on the Short Period Mode of the Simulator With Time-Frequency Combination

Based on flight simulation data and measured data, the article extracted short cycle time of correlating parameters curve from a certain type of flight simulators. And in the time domain, using the average standardized distance test method and gray correlation analysis method for this type of simulator simulation data. In the frequency domain, the classical spectral estimation method is used for quantitative detection, and the consistency test of flight simulation data and flight parameters is realized. Then the evidence theory is improved and thus the synthesized type simulator short cycle when the correlating parameters and frequency domain the consistency of the test results, verify the longitudinal short-period mode type simulator fidelity is better.

Nonlinear aircraft system identification using artificial neural networks enhanced by empirical mode decomposition

This paper aims to improve the performance of artificial neural networks used for the aircraft system identification by taking flight dynamic characteristics into consideration. In the proposed method, flight dynamic modes are recognized, isolated, and inputted individually to feed-forward neural networks. This method has several advantages such as being adaptive, involving all observable modes in the identification process, considering interactions between longitudinal and lateral-directional modes, and reducing noise effects. Simulated and real flight data of the HARV aircraft at high-angle of attack maneuvers are employed to train the neural networks and evaluate them. Results demonstrate improved accuracy and generality of the proposed method in comparison with the conventional ones.

2017 WSGC Elijah High-Altitude Balloon Payload

The 2017 WSGC Elijah High-Altitude Balloon Payload Fellowship focused on three different topics for high altitude research: Modular Payload Design, Balloon Dynamics, and Energy Harvesting. A modular payload system was created using advanced manufacturing methods, which improved assembly and field operation. Minor structural fracturing was observed upon recovery. All instrumentation recovered were functioning. Vertical flight dynamics of a high-altitude balloon were studied to create a model that was compared against experimental data. Predictions did not accurately replicate GPS altitude data, possibly due to incorrect internal-balloon pressure readings and underlying assumptions. Habitability of high-altitude environments were explored by monitoring insect analog in pressurized environment. A slow pressure leak induced insects into a comatose state. Radiation was detected visually with camera.Investigated energy generation from balloon kinematics. Flight data not obtained but flight simulation data produced average voltage = 0.0039 V and total energy = 245.13 J.

Parameter Estimation of Unstable Aircraft using Extreme Learning Machine

<p class="p1">The parameter estimation of unstable aircraft using extreme learning machine method is presented. In the past, conventional methods such as output error method, filter error method, equation error method and non-conventional method such as artificial neural-network based methods have been used for aircraft’s aerodynamic parameter estimation. Nowadays, a trend of finding an accurate nonlinear function approximation is required to represent the aircraft’s equations-of-motion. Such type of nonlinear function approximation is usually achieved using artificial neural-network which is trained with the aircraft input-output flight data using a training algorithm. The accuracy of estimated parameters, which is achieved using the trained network, is highly dependent on the generalisation capability of the network which can be improved using extreme learning machine based network in contrast to artificial neural-network. To estimate the unstable aircraft parameters from the simulated flight data, Gauss-Newton based optimisation method has been used with a predefined aerodynamic model using the trained network. Further, the confidence of the estimated parameters has been shown in comparison to that of the standard parameter estimation methods in terms of the Cramer-Rao bounds.</p>

Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records

Among the operations in the general-aviation community, one of the most important objectives is to improve safety across all flight regimes. Flight-data-monitoring or flight-operations-quality-assurance programs have percolated in the general-aviation sector with the aim of improving safety by analyzing and evaluating flight data. Energy-based metrics provide measurable indications of the energy state of the aircraft, and can be viewed as an objective currency to evaluate various safety-critical conditions. The use of data-mining techniques for safety analysis, incident examination, and fault detection is gaining traction in the aviation community. In this paper, a generic methodology is presented for identifying anomalous flight-data records from general-aviation operations in the approach-and-landing phase. Energy-based metrics, identified in previous work, are used to generate feature vectors for each flight-data record. Density-based clustering and one-class classification are then used together for anomaly detection using energy-based metrics. A demonstration of this methodology on a set of actual flight-data records from routine operations, as well as simulated flight data, is presented, highlighting its potential for retrospective safety analysis. Anomaly detection using energy metrics, specifically, is a novel application presented here.

Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

Development of Occupant-Preferred Landing Profiles for Personal Aerial Vehicles

With recent increased interest in autonomous vehicles and the associated technology, the prospect of realizing a personal aerial vehicle seems closer than ever. However, there is likely to be a continued requirement for any occupant of an air vehicle to be comfortable with both the automated portions of the flight and their ability to take manual control as and when required. This paper, using the approach to landing as an example maneuver, examines what a comfortable trajectory for personal aerial vehicle occupants might look like. Based upon simulated flight data, a “natural” flight trajectory is designed and then compared to constant deceleration and constant optic flow descent profiles. It is found that personal aerial vehicle occupants with limited flight training and no artificial guidance follow the same longitudinal trajectory as has been found for professionally trained helicopter pilots. Further, the final stages of the approach to hover can be well described using the Tau theory. For automatic flight, personal aerial vehicle occupants prefer a constant deceleration profile. For approaches flown manually, the newly designed natural profile is preferred.

Optimal Control Framework for Cruise Economy Mode of Flight Management Systems

The main contribution of this paper is to propose optimal and suboptimal control solutions to the cruise economy mode problem in a flight management system for flights below the drag divergence Mach number. The problem is formulated as an optimization of a functional that trades off the fuel and time-related costs of a flight using a (crew-supplied) cost index CI. A novel approach is proposed based on solving the problem analytically using a combination of Pontryagin’s maximum principle and the Hamilton–Jacobi–Bellman equation. A suboptimal analytical solution for the true airspeed is obtained in state feedback form, which reduces to the well-known optimal solution for maximum range when the cost index vanishes. An analytical solution for the speed target as a function of the cost index gives physical insight, allows one to analytically compute sensitivities, and eliminates the need to have a performance database to store the optimal speed schedules in the system. An extension shows that the approach is valid when the aircraft is turning with a small bank angle. Overall, this work provides not only a very efficient means of implementing the optimal speed schedules in an onboard flight management system for flights below the divergence Mach number, but also extends the theory of aircraft performance to the more general case based on a nonzero cost index. The new results are compared with flight simulation data for an A320 Airbus aircraft.

Development and Application of a Reconfigurable Engineering Flight Simulator at Ryerson University

Flight simulators can recreate aircraft flights for flight training and aircraft design. This paper reviews existing engineering flight simulators and introduces the development of a multi-purpose reconfigurable engineering flight simulator at Ryerson University. The multi-purpose engineering flight simulator named the Ryerson Fixed Base Simulator (RFBS) has been designed and built to teach and initiate research projects in the area of aircraft design, flight simulation, pilot training, and flight data analysis. It consists of three 46 inch high definition screens and six 22 inch touch screen panels to represent the instrument panel, the centre console, and the overhead panels of an actual aircraft flight deck. Several low-cost, commercial flight simulation software were tested and X-Plane was selected as the main flight simulation tool. This paper also introduces a research that utilizes the RFBS and a commercial flight analysis software. The objective of the research was to develop a flight data conversion methodology. A case study was commenced to verify the work flow of flight data generation and analysis with an example of hard-landing analysis.

A novel guidance and navigation system for MAVs capable of autonomous collision-free entering of buildings

Micro Aerial Vehicles for autonomous explorations of hazardous areas are predestined to support emergency and rescue forces. Especially the autonomous access to buildings is highly demanding due to insufficient GNSS reception in urban terrain and narrow passageways into buildings. Thus, this paper presents a complete flight system, consisting of guidance, navigation and control subsystems. All these elements are designed to enable safe flights into buildings. The guidance subsystem is divided into two parts. The vision based guidance part is manoeuvring the MAV on an intermediate position in front of the building. The potential field based guidance part enables the MAV to fly inside the buildings without having any collisions. For that, neither any prior knowledge about the building structure, nor any maps are necessary. To provide the flight guidance with information about the actual kinematic state of the MAV an accurate and robust navigation system not depending on GNSS measurements is used. The complete system is evaluated using simulated flight data.

Adaptively Reevaluated Bayesian Localization (ARBL): A novel technique for radiological source localization

We present a novel technique for the localization of radiological sources in urban or rural environments from an aerial platform. The technique is based on a Bayesian approach to localization, in which measured count rates in a time series are compared with predicted count rates from a series of pre-calculated test sources to define likelihood. This technique is expanded by using a localized treatment with a limited field of view (FOV), coupled with a likelihood ratio reevaluation, allowing for real-time computation on commodity hardware for arbitrarily complex detector models and terrain. In particular, detectors with inherent asymmetry of response (such as those employing internal collimation or self-shielding for enhanced directional awareness) are leveraged by this approach to provide improved localization. Results from the localization technique are shown for simulated flight data using monolithic as well as directionally-aware detector models, and the capability of the methodology to locate radioisotopes is estimated for several test cases. This localization technique is shown to facilitate urban search by allowing quick and adaptive estimates of source location, in many cases from a single flyover near a source. In particular, this method represents a significant advancement from earlier methods like full-field Bayesian likelihood, which is not generally fast enough to allow for broad-field search in real time, and highest-net-counts estimation, which has a localization error that depends strongly on flight path and cannot generally operate without exhaustive search.

Simulation-Based Helicopter Mission Effectiveness Evaluation Method

It is always difficult to obtain a reliable result of helicopter mission effectiveness evaluation (HMEE) by traditional methods. In order to solve the problem, this paper compared and analysed advantages and disadvantages of traditional effectiveness evaluation methods, and presented a simulation-based helicopter mission effectiveness evaluation method. The method is based on the simulation data. The method combines with advantages of multi parameter evaluation method, computer simulation evaluation method and logarithmic evaluation method. By using the reliable helicopter flight simulation data and an objective weight coefficient calculation methods, we can get accurate and reliable helicopter mission effectiveness value. Research proves that this method can be applied in the field of effectiveness evaluation.

Evaluation of a novel attitude estimation algorithm for a high performance fighter aircraft

This paper discusses the evaluation of a novel attitude estimation algorithm (Kamali et al. (2013)) for a high performance fighter aircraft. This algorithm employs a new modelling approach in the Extended Kalman Filtering (EKF) framework to estimate aircraft attitude information without using forward acceleration sensor, Global Positioning System (GPS) sensors or magnetometer. Evaluation of the algorithm is conducted using flight data from a high performance fighter aircraft and using flight simulation data. Estimation results during various manoeuvres such as full rolls, inverted loops, split-S manoeuvres, steep climbs, and dives, are studied. Effect of different wind perturbations (gust, shear, turbulence, etc.), on estimation results, is also studied. Results using flight data are compared with those obtained from a GPS-assisted Inertial Navigation System (INS), whereas for results using simulation data, the simulation itself provides true values. Conclusions are drawn for the performance of the algorithm based on simulation and flight data.

Pilot Control Behavior Analysis Using Cutoff Frequency and Power Frequency for a Civil Transport Aircraft Encountering Turbulence Based on Flight Simulation

Atmospheric turbulence is one of the leading causes of serious personal injuries in nonfatal accidents of commercial aircraft and poses a significant hazard to aircraft in the terminal area. The pilot control behavior, workload and its difference from the normal flight situation also have essential impact on flight safety when the airplane encountered turbulence. In this paper, a unique frequency domain method, which used the cutoff frequency and the power frequency as the key metrics, was utilized to analyze the pilot control behavior in turbulence environment for a civil transport aircraft based on piloted flight simulation data. The results show that the cutoff frequency and power frequency consider both the frequency of pilot activity and the level of the activity at the same time, and could be a useful tool to analyze the pilot control behavior.

Feasible path planning for fixed-wing UAVs using seventh order Bézier curves

Abstract This study presents a novel methodology for generating smooth feasible paths for autonomous aerial vehicles in the three-dimensional space based on a variation of the Spatial Quintic Pythagorean Hodographs curves. Generated paths must satisfy three main constraints: (i) maximum curvature, (ii) maximum torsion and (iii) maximum climb (or dive) angle. A given path is considered to be feasible if the main kinematic constraints of the vehicle are not violated, which is accomplished in our approach by connecting different waypoints with seventh order Bézier curves. This also indirectly insures the smoothness of the vehicle’s acceleration profile between two consecutive points of the curve and of the entire path by controlling the curvature values at the extreme points of each composing Bézier curve segment. The computation of the Pythagorean Hodograph is cast as an optimization problem, for which we provide an algorithm with fast convergence to the final result. The proposed methodology is applicable to vehicles in three-dimensional environments, which can be modeled presuming the imposed constraints. Our methodology is validated in simulation with real parameters and simulated flight data of a small autonomous aerial vehicle.

Aerodynamic Derivatives Identification Using a Non-Conservative Robust Kalman Filter

A non-conservative robust Kalman filter (NCRKF) is applied to flight data to identify the aerodynamic derivatives of an unmanned autonomous vehicle (UAV). The NCRKF is formulated using UAV lateral motion data and then compared with results from the conventional Kalman filter (KF) and the recursive least square (RLS) method. A superior performance for the NCRKF is demonstrated by simulation and real flight data. The NCRKF is especially effective in large uncertainties in vehicle modeling and in measuring flight data. Thus, it is expected to be useful in missile and aircraft parameter identification.