Advantages of Neural Network Based Air Data Estimation for Unmanned Aerial Vehicles (original) (raw)
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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this s...
Novel Neural Architecture for Air Data Angle Estimation
Springer eBooks, 2013
This paper presents a novel architecture for air-data angle estimation. It represents an effective low-cost low-weight solution to be implemented in small, mini and micro Unmanned Aerial Vehicles (UAVs). It can be used as a simplex sensor or as a voter in a dual-redundant sensor systems, to detect inconsistencies of the main sensors and accommodate the failures. The estimator acts as a virtual sensor processing data derived from an Attitude Heading Reference System (AHRS) coupled with a dynamic pressure sensor. This novel architecture is based on the synergy of a neural network and of an ANFIS filter which acts on the noise-corrupted data,cancelling the noise contribution without interfering with the turbulence frequencies, which must be preserved as key information for the AFCS activity.
Increase the aviation efficiency of UAVs using artificial neural networks
Advanced Information Systems, 2017
Purpose. It is known that the flight of the UAV is conducted by sensors that transmit the performance of the UAV and on the basis of this information is controlled on the UAV and give them the orders which are necessary to perform the task of flying UAV. and normal these faults occur during the flight of unmanned air vehicle (UAV), according to the concepts of aviation is a very critical situation that affects the completion of the mission. These faults are mainly due to failure in the sensors, which can be divided into. Flight Situation is about the flying situation of the aircraft, such as (heading, altitude, airspeed, and vertical speed and angle of attack sensors. And Flight Control Situation, this is about the flight control surfaces such as (rudder, aileron, and elevator deflection), pitch attitude, and roll attitude sensors. This paper presents an effective technique to ensure that the sensors can operate with high efficiency. Methods. Two different approaches are used in this work. The first approach is Neural Network (NN) based tool for the modeling, simulation and analysis of aircraft (SFDIA), sensors failure, detection, and identification and accommodation problem. The second approach is Neural Network trained with the (EMRAN) algorithms which is a set of conditions that decide how the (EMRAN) structure should be adapted to better suit the training data. Results. The results from the modeling process and analysis of aircraft sensors showed that the neural network based tool (SFDIA) and the (EMRAN) algorithms are able to show high-resolution results in the behavior of sensors and hence in the (UAV) behavior. Conclusions. The capabilities of (SFDIA) are a consequence of the extensive modularity of the whole simulation tool. It allows an easy change of unmanned air vehicle (UAV), dynamics and feedback control law as well as Neural Network (NN) estimators and (SFDIA) scheme. K e ywor d s : Unmanned aircraft vehicle; Sensor fault detection; Fault diagnosis; Aircraft sensors modeling and simulation.
Aerodynamic angle estimation: comparison between numerical results and operative environment data
CEAS Aeronautical Journal
Several architectures exist to measure aerodynamic angles based on physical sensors. As far as Unmanned Aerial Vehicle (UAV) is concerned, traditional systems hardly comply with reliability and redundancy requirements due to size and weight limitations. A patented virtual sensor, based on Artificial Neural Network (ANN) techniques, named Smart-Air Data, Attitude and Heading Reference System (Smart-ADAHRS) has been investigated as a good estimator for aerodynamic angles in simulated environment. This paper focuses on flight testing procedures in operative environment and data processing for the Smart-ADAHRS validation with real data. As many factors interfere during the generation of the ANN training set, an accurate choice and integration of the Flight Test Instrumentation (FTI) system components becomes crucial. A comprehensive description has been included about the FTI equipment and its influence on the neural network performance. Differences between numerical simulation and operative environment data are detailed as final aim of this work. At the end, feasible solutions are suggested to solve the typical gap between virtual and real scenario, both in terms of data analysis and neural network architecture.
Air Data Computation Using Neural Networks
Journal of Aircraft, 2008
The paper deals with the use of neural networks for the determination of pressure altitude and Mach number of a fly-by-wire high-performance aircraft during flight. In previous works the authors developed a methodology based on polynomial calibration functions for the determination of such flight parameters, together with the angles of attack and sideslip. Such an approach provided successful results, but the use of different polynomial functions in different areas was needed to map the entire flight envelope. The fading methodologies for the management of polynomial functions overlap and considerably increased both procedure complexity and the time to spent for the procedure tuning. In particular, the calibration functions related to the Mach number and static-pressure estimation are susceptible to these problems because of their high nonlinearity. The alternative approach studied in this paper, based on neural networks, provides a level of accuracy comparable with that of polynomial functions. However, such an approach is simpler, because it allows the entire flight envelope to be mapped by means of a single network for each output parameter, and so it eliminates the fading problems. In addition, the new procedure is extremely easier to tune when new data from flight tests are available. This is a very important point, because several versions of the air data computation algorithms are generally to be developed in parallel with the flight-envelope enlargement of a new aircraft.
Design, Implementation and Evaluation of a Neural Network Based Quadcopter UAV System
In this paper, a quadcopter UAV system based on neural network enhanced dynamic inversion control is proposed for multiple real-world application scenarios. A Sigma-Pi neural network (SPNN) is used as the compen-sator to reduce the model error and improve the system performance in the presence of uncertainties of UAV dynamics, payload and environment. Besides, we present a technical framework for fast and robust implementation of multipurpose UAV systems, and develop a UAV control system evaluation testbed using a high-precision optical motion capture system. Both the simulation and experiment results demonstrate that the SPNN can reduce inversion errors related to UAV parameter uncertainties as well as tracking errors related to unknown disturbances and unmodeled dynamics. With the help of an online NN learning mechanism, the entire system can achieve much higher accuracy in attitude and trajectory control than conventional PID-based control systems under varying flight conditions.
Sensors
Heterogeneity of the small aircraft category (e.g., small air transport (SAT), urban air mobility (UAM), unmanned aircraft system (UAS)), modern avionic solution (e.g., fly-by-wire (FBW)) and reduced aircraft (A/C) size require more compact, integrated, digital and modular air data system (ADS) able to measure data from the external environment. The MIDAS project, funded in the frame of the Clean Sky 2 program, aims to satisfy those recent requirements with an ADS certified for commercial applications. The main pillar lays on a smart fusion between COTS solutions and analytical sensors (patented technology) for the identification of the aerodynamic angles. The identification involves both flight dynamic relationships and data-driven state observer(s) based on neural techniques, which are deterministic once the training is completed. As this project will bring analytical sensors on board of civil aircraft as part of a redundant system for the very first time, design activities docume...
ANFIS Based Attitude Dynamics Identification of Unmanned Aerial Vehicle
This paper presents a modified fuzzy neural network (MFNN) for the in-flight estimation of nonlinear, highly coupled and time varying attitude dynamics of unmanned aerial platforms. The hybrid adaptive learning algorithm combining recursive least squares and gradient descent has been used to update the linear and nonlinear parameters of MFNN. The performance of the devised architecture as an attitude dynamics identifier is validated in real time through the test flight of Kadet Senior UAV. Mean square error of 0.018 degrees between the actual pitch and output of MFNN based pitch identifier proves the applicability of approach.
An Adaptive Neuro-Fuzzy-based Multisensor Data Fusion applied to real-time UAV autonomous navigation
Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018), 2018
The world trend in employing UAVs and drones is remarkable. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. However, they depend on positioning systems that may be fallible. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming at safe navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. Nonetheless, its high computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a Data Fusion application based on Computational Intelligence – Adaptive-Network-Based Fuzzy Inference System (ANFIS) – which is able to improve the accuracy of such position estimation systems.
Technology audit and production reserves, 2021
The object of research is the process of controlling the trajectory of unmanned aerial vehicles (UAVs) in autonomous flight mode based on neural network algorithms. The study is based on the application of numerical-analytical approach to the selection of modern technical solutions for the construction of standard models of platformless inertial navigation systems (BINS) for micro and small UAVs, followed by support for assumptions. The results of simulation in the Matlab environment allowed to simulate the operation of the UAV control system based on MEMS technology (using microelectromechanical systems) and Arduino microcomputers. It was also possible to experimentally determine the nature of the influence of the structure of the selected neural network on the process of formation of navigation data during the disappearance of the GPS signal. Thus, to evaluate the effectiveness of the proposed solutions for the construction of BINS, a comparative analysis of the application of two...