Unmanned Aerial Vehicle Flight Control Research Articles

Control of longitudinal flight dynamics of a fixedwing UAV using LQR, LQG and nonlinear control

This paper aim is to present a comparative study between Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG) and nonlinear controllers for pitch control of a fixed-wing Unmanned Aerial Vehicle (UAV). Due to a good stability margin and strong robustness LQR has been selected. LQG was chosen because is able to overcome external disturbances. Kalman Filter controller was also introduced to the fixed-wing UAV flight control. Further, we designed an autopilot that controls the pitch angle of the fixed-wing UAV. In the end, the control laws are simulated in Matlab/Simulink. The results obtained are compared to see which method is faster, more reliable and more robust.

Simultaneous Design of a Small UAV (Unmanned Aerial Vehicle) Flight Control System and Lateral State Space Model

In this study, the design of a small unmanned aerial vehicle (UAV) and the real-time application of the flight control system and lateral state-space model were investigated. For this purpose, an UAV production was carried out, which was assembled from different locations at certain intervals to the wing and tail set body and moved back and forth before the flight. An autopilot was then used which allowed the change of P, I, D values ​​between 1 and 100. First of all, we obtained a lateral state space model of the UAV and obtained a simulation model of Unmanned Aerial Vehicle. At the same time, the block diagram of the autopilot system was extracted and modeled in MATLAB / Simulink environment. Afterwards, SPSA developed a cost function consisting of ascent, seating time and maximum overrun, and the Unmanned Aircraft and autopilot system were redesigned simultaneously to minimize this cost function. High performance is easily observed in simulation responses and real flights.

Design of Simulation Test Platform for UAV Flight Control System

According to the analysis on the composition as well as functions of flight control system of unmanned aerial vehicle (UAV), the computer simulation test method of flight control and management based on simulation test platform is introduced, with the focus on fault injection and test method. Thereby putting forward the construction principle and test method for simulation test environment of flight control system. In the end, the follow-up development of the dynamic simulation test of UAV flight control system is pointed out.

Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system

Owing to the increase in civil applications using quadcopters, commercial flight control systems such as Pixhawk are a popular solution to provide the sensing and control functions of an unmanned aerial vehicle (UAV). A low-cost global navigation satellite system (GNSS) receiver is crucial for the low-cost flight control system. However, the accuracy of GNSS positioning is severely degraded by the notorious multipath effect in mega-urbanized cities. The multipath effect cannot be eliminated but can be mitigated; hence, the GNSS/inertial navigation system (INS) integrated navigation is a popular approach to reduce this error. This study proposes an adaptive Kalman filter for adjusting the noise covariance of GNSS measurements under different positioning accuracies. The adaptive tuning is based on a proposed accuracy classification model trained by a supervised machine-learning method. First, principal component analysis is employed to identify the significant GNSS accuracy related features. Subsequently, the positioning accuracy model is trained based on a random forest learning algorithm with the labeled real GNSS dataset encompassing most scenarios concerning modern urban areas. To reduce the cases of misclassifying the GNSS accuracy, a fuzzy logic algorithm is employed to consider the GNSS accuracy propagation. Additionally, the process noise covariance of the INS is determined using the Allan variance analysis. The positioning performance of the proposed adaptive Kalman filter is compared with both a conventional Kalman filter and the positioning solution provided by the commercial flight control system, Pixhawk 2. The results show that the proposed adaptive Kalman filter using random forest with fuzzy logic can achieve a better classification of GNSS accuracy compared to the others. The overall positioning result improved by approximately 50% compared with the onboard solution.

Hierarchical motion consistency constraint for efficient geometrical verification in UAV stereo image matching

This paper proposes a strategy for efficient geometrical verification in unmanned aerial vehicle (UAV) image matching. First, considering the complex transformation model between correspondence set in the image-space, feature points of initial candidate matches are projected onto an elevation plane in the object-space, with assistant of UAV flight control data and camera mounting angles. Spatial relationships are simplified as a 2D-translation, in which a motion establishes the relation of two correspondence points. Second, a hierarchical motion consistency constraint, termed HMCC, is designed to eliminate outliers from initial candidate matches, which includes three major steps, namely the global direction consistency constraint, the local direction-change consistency constraint and the global length consistency constraint. To cope with scenarios with high outlier ratios, the HMCC is achieved by using a voting scheme. Finally, an efficient geometrical verification strategy is proposed by using the HMCC as a pre-processing step to increase inlier ratios before the consequent application of the basic RANSAC algorithm. The performance of the proposed strategy is verified through comprehensive comparison and analysis by using real UAV datasets captured with different photogrammetric systems. Experimental results demonstrate that the generated motions have noticeable separation ability, and the HMCC-RANSAC algorithm can efficiently eliminate outliers based on the motion consistency constraint, with a speedup ratio reaching to 6 for oblique UAV images. Even though the completeness sacrifice of approximately 7% of points is observed from image orientation tests, competitive orientation accuracy is achieved from all used datasets. For geometrical verification of both nadir and oblique UAV images, the proposed method can be a more efficient solution.

NEAR REAL-TIME GEOREFERENCE OF UMANNED AERIAL VEHICLE IMAGES FOR POST-EARTHQUAKE RESPONSE

Abstract. The rapid collection of Unmanned Aerial Vehicle (UAV) remote sensing images plays an important role in the fast submitting disaster information and the monitored serious damaged objects after the earthquake. However, for hundreds of UAV images collected in one flight sortie, the traditional data processing methods are image stitching and three-dimensional reconstruction, which take one to several hours, and affect the speed of disaster response. If the manual searching method is employed, we will spend much more time to select the images and the find images do not have spatial reference. Therefore, a near-real-time rapid georeference method for UAV remote sensing disaster data is proposed in this paper. The UAV images are achieved georeference combined with the position and attitude data collected by UAV flight control system, and the georeferenced data is organized by means of world file which is developed by ESRI. The C # language is adopted to compile the UAV images rapid georeference software, combined with Geospatial Data Abstraction Library (GDAL). The result shows that it can realize rapid georeference of remote sensing disaster images for up to one thousand UAV images within one minute, and meets the demand of rapid disaster response, which is of great value in disaster emergency application.

An Alternative Parity Space-Based Fault Diagnosability Analysis Approach for Linear Discrete Time Systems

This paper deals with the problem of parity space-based fault diagnosability analysis for linear discrete time systems. The main contribution lies in the design of the fault diagnosability evaluation indexes by combining the distance difference information with the direction difference information between the residuals in different cases. Under the assumption that the unknown inputs are random white noises, the residual generation is achieved by the parity space-based fault diagnosis approach. Based on this, the problem of fault diagnosability analysis is formulated as a bank of evaluation problems of the difference information between two residuals in fault-free case or in different faulty cases. Then, the fault diagnosability evaluation indexes are proposed based on the integrated design of the distance similarity function and the direction similarity function between different residuals, and the improved fault isolation conditions are constructed to provide an auxiliary index for fault diagnosability analysis. A simulation is carried out on a fixed-wing unmanned aerial vehicle flight control system, and the results demonstrate that the proposed method can achieve fault diagnosability analysis accurately for the linear discrete time systems.

Autonomous UAV Flight Control for GPS-Based Navigation

This paper proposes an unmanned aerial vehicle (UAV) flight control method where a graph-based path is generated after the collected UAV flight data by a pilot are analyzed. UAV flights are planned by using hierarchical A* search algorithms based on graph-based generated flight paths to take images at multiple surveillance points. Generating a graph-based path makes it possible for UAVs to fly autonomously along paths shorter than that of the pilot collecting UAV flight data given that the shorter paths can be derived by connecting partially flied paths. A* search algorithms can be applied hierarchically to a graph-based path that contains circulation paths. The proposed method was experimentally verified through an analysis of the collected UAV flight data to generate graph-based and planned paths. The pilot flew the UAV six times and obtained 8115 UAV flight data points. The generated graph-based path included 17 monitoring points for taking surveillance images and 90 intermediate flight points. The length of the flight paths collected by six time flights was 1364.32 m, and the length of the flight paths by the proposed method was 764.27 m. Given that 8115 flight points were collected and 109 flight points were selected by the proposed method, the complexity of the generated graph-based path consisted of flight points was reduced to 1.34% by hierarchical A* search algorithms.

Reconfigurable Fault Tolerant Flight Control for UAV with Pseudo-Inverse Technique

Abstract In this study, the parameter identification based fault tolerant control of an unmanned aerial vehicle (UAV) dynamics is presented. When faults occur in the actuator, then the elements of the control distribution matrix (control derivatives) change. The augmented Kalman filter (KF) is used in order to identify the control distribution matrix elements; thus, the control reconfiguration is carried out using this identified control distribution matrix. A reconfigurable pseudo-inverse controller is designed for the modeled UAV. The control derivatives are identified by the augmented KF and pseudo-inverse controller is reconfigured for the identified control distribution matrix. In simulations, the linearized model of longitudinal dynamics of ZAGI UAV is considered, and the performance of the proposed system identification and reconfigurable control techniques are examined for this model. For this purpose the control derivatives related to elevator and thrust are changed in accordance with faulty system condition and the augmented KF based reconfigurable control algorithm using pseudo-inverse technique is investigated.

Efficient structure from motion for oblique UAV images based on maximal spanning tree expansion

The primary contribution of this paper is an efficient Structure from Motion (SfM) solution for oblique unmanned aerial vehicle (UAV) images. First, an algorithm, considering spatial relationship constraints between image footprints, is designed for match pair selection with the assistance of UAV flight control data and oblique camera mounting angles. Second, a topological connection network (TCN), represented by an undirected weighted graph, is constructed from initial match pairs, which encodes the overlap areas and intersection angles into edge weights. Then, an algorithm, termed MST-Expansion, is proposed to extract the match graph from the TCN, where the TCN is first simplified by a maximum spanning tree (MST). By further analysis of the local structure in the MST, expansion operations are performed on the vertices of the MST for match graph enhancement, which is achieved by introducing critical connections in the expansion directions. Finally, guided by the match graph, an efficient SfM is proposed. Under extensive analysis and comparison, its performance is verified by using three oblique UAV datasets captured with different multi-camera systems. Experimental results demonstrate that the efficiency of image matching is improved, with speedup ratios ranging from 19 to 35, and competitive orientation accuracy is achieved from both relative bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA with GCPs. At the same time, images in the three datasets are successfully oriented. For the orientation of oblique UAV images, the proposed method can be a more efficient solution.

Simultaneous multicopter-based air sampling and sensing of meteorological variables

Abstract. The state and composition of the lowest part of the planetary boundary layer (PBL), i.e., the atmospheric surface layer (SL), reflects the interactions of external forcing, land surface, vegetation, human influence and the atmosphere. Vertical profiles of atmospheric variables in the SL at high spatial (meters) and temporal (1 Hz and better) resolution increase our understanding of these interactions but are still challenging to measure appropriately. Traditional ground-based observations include towers that often cover only a few measurement heights at a fixed location. At the same time, most remote sensing techniques and aircraft measurements have limitations to achieve sufficient detail close to the ground (up to 50 m). Vertical and horizontal transects of the PBL can be complemented by unmanned aerial vehicles (UAV). Our aim in this case study is to assess the use of a multicopter-type UAV for the spatial sampling of air and simultaneously the sensing of meteorological variables for the study of the surface exchange processes. To this end, a UAV was equipped with onboard air temperature and humidity sensors, while wind conditions were determined from the UAV's flight control sensors. Further, the UAV was used to systematically change the location of a sample inlet connected to a sample tube, allowing the observation of methane abundance using a ground-based analyzer. Vertical methane gradients of about 0.3 ppm were found during stable atmospheric conditions. Our results showed that both methane and meteorological conditions were in agreement with other observations at the site during the ScaleX-2015 campaign. The multicopter-type UAV was capable of simultaneous in situ sensing of meteorological state variables and sampling of air up to 50 m above the surface, which extended the vertical profile height of existing tower-based infrastructure by a factor of 5.

Integrated method for the UAV navigation sensor anomaly detection

The rapid development of unmanned aerial vehicles (UAVs) has made great progress for its widespread uses in military and civilian applications in recent years. On-board integrated navigation sensors are essential for UAV flight control systems in that they must operate with robustness and reliability. To achieve this, timely and effectively anomaly detection capabilities for the estimated UAV status from the integrated navigation sensors are required to ensure the UAV flight safety. Extraction of the anomaly information from the real-time navigation sensors and designing a robust and reliable anomaly detection algorithm are major issues for the UAV navigation sensor anomaly detection. This study introduces a novel integrated algorithm for detecting UAV on-board navigation sensor anomaly, by combining particle filter (PF) estimated state residuals with fuzzy inference system (FIS) decision system. The residual information is obtained based on the difference between the collected Global Positioning System measurements and high accuracy PF estimates. The indicators derived from the PF residuals are further made as inputs for the FIS system to output the different anomaly levels. The simulation and filed test results have demonstrated the effectiveness and efficiency of the proposed anomaly detection method in terms of timeliness, recall and precision.

Unmanned Aerial Vehicle Flight Control Evaluations Under Sensor and Actuator Faults

Nonlinear aircraft controlled by its linearized model based controller will be asymptotically stable if its trajectories originating from various initial conditions are all contained in the stability region (or the region of attraction). In such a stable aircraft, suppose a sensor or an actuator fault occurs. Depending upon the fault size, the stability region is modified. As a result, the trajectories for a potential failure to contain in the modified stability region could lead to flight control degradation. In this paper, first, a stability margin is determined to separate an acceptable fault size from an unacceptable fault. Secondly, with an acceptable fault size, the aircraft will remain stable in distorted stability regions. In this paper, admissible control inputs which generate the perturbed trajectories in the distorted stability regions is presented for safe unmanned aerial vehicle flight control evaluations. A three degree of freedom aircraft in pitch plane is considered to illustrate the stability margins and control inputs that are safe to operate the damaged aircraft.

Automatic Reverse Engineering of Private Flight Control Protocols of UAVs

The increasing use of civil unmanned aerial vehicles (UAVs) has the potential to threaten public safety and privacy. Therefore, airspace administrators urgently need an effective method to regulate UAVs. Understanding the meaning and format of UAV flight control commands by automatic protocol reverse-engineering techniques is highly beneficial to UAV regulation. To improve our understanding of the meaning and format of UAV flight control commands, this paper proposes a method to automatically analyze the private flight control protocols of UAVs. First, we classify flight control commands collected from a binary network trace into clusters; then, we analyze the meaning of flight control commands by the accumulated error of each cluster; next, we extract the binary format of commands and infer field semantics in these commands; and finally, we infer the location of the check field in command and the generator polynomial matrix. The proposed approach is validated via experiments on a widely used consumer UAV.

Real-Time Reliability Verification for UAV Flight Control System Supporting Airworthiness Certification.

In order to verify the real-time reliability of unmanned aerial vehicle (UAV) flight control system and comply with the airworthiness certification standard, we proposed a model-based integration framework for modeling and verification of time property. Combining with the advantages of MARTE, this framework uses class diagram to create the static model of software system, and utilizes state chart to create the dynamic model. In term of the defined transformation rules, the MARTE model could be transformed to formal integrated model, and the different part of the model could also be verified by using existing formal tools. For the real-time specifications of software system, we also proposed a generating algorithm for temporal logic formula, which could automatically extract real-time property from time-sensitive live sequence chart (TLSC). Finally, we modeled the simplified flight control system of UAV to check its real-time property. The results showed that the framework could be used to create the system model, as well as precisely analyze and verify the real-time reliability of UAV flight control system.

Методика синтеза программно-аппаратных комплексов автоматизированной разработки систем управления полетом беспилотных летательных аппаратов

Методика синтеза программно-аппаратных комплексов автоматизированной разработки систем управления полетом беспилотных летательных аппаратов

Control computers diagnostics for UAV flight control system

Purpose The purpose of this paper is to present the topic of control computers diagnostics. They are part of an unmanned aerial vehicle (UAV) control system implemented in a modified version of MP-2 Czajka aircraft. Design/methodology/approach The algorithms were designed as a basic version of the diagnostic system. The system is open and will be developed. Findings First results show that the diagnostic system works properly. The system is easy for implementation and burdens the control computers only insignificantly. Research limitations/implications The system presented can detect only computers out of work. In its present version, it cannot detect such errors as improper calculations of control signals. After first in-flight testing, the system will be further developed. Practical implications The diagnostic system is implemented in an UAV technology demonstrator. Originality/value The designed system is the part of an UAV control system, designed for ground observation. Such technology demonstrator and flying laboratory enable different type of research in the area of aviation.

Active Fault-Tolerant Control of UAV Dynamics against Sensor-Actuator Failures

AbstractIn this paper, an active fault-tolerant flight control system against sensor/actuator failures for unmanned aerial vehicles (UAVs) is proposed. First, an approach to detecting and isolating UAV sensor failures affecting the mean of the Kalman filter (KF) innovation sequence is proposed. Second, an adaptive two-stage linear Kalman filtering algorithm is used to isolate the sensor and actuator faults and to estimate the loss of control effectiveness and the magnitude of degree of stuck faults in a UAV model. Control effectiveness factors and stuck magnitudes are used to quantify the faults entering the UAV flight control system through actuators. In the case of a sensor fault, the faulty sensor is isolated, and the KF that ignores the feedback from the faulty sensor is built. If the fault is an actuator fault, then the actuator fault isolation and identification are performed using the adaptive two-stage KF. The parameters of the feedback controller are tuned by the control reconfiguration procedure...

FAILURE ANALYSIS OF A UAV FLIGHT CONTROL SYSTEM USING MARKOV ANALYSIS

Failure analysis of a flight control system proposed for Air Force Institute of Technology (AFIT) Unmanned Aerial Vehicle (UAV) was studied using Markov Analysis (MA). It was perceived that understanding of the number of failure states and the probability of being in those state are of paramount importance in order to ensure safety flight of the UAV. Thus, in this study the number of working states and the probability of being in those states for the proposed UAV flight control system was accessed using Markov Analysis (MA). Specifically, the truncated transition state diagram of the UAV flight control system considered in this study was developed and differential equations associated with the transition state diagram were also generated. Laplace transformation technique was used to solve the differential equations. From the findings of the study, the need for design improvement was established. http://dx.doi.org/10.4314/njt.v35i1.24

A Perceptual Computing based Gesture Controlled Quadcopter for Visual Tracking and Transportation

One of the fundamental challenges faced by an inexperienced user in portable unmanned aerial vehicle (UAV) such as quadcopters is flight control, often leading to crashes. Addressing this challenge, and leveraging upon the technological advancement in perceptual computing and computer vision, this research presents a modular system that allows for hand gesture based flight control of UAV, alongside a transport mechanism for portable objects. In addition to ascertain smooth flight control by avoiding obstacles in navigation path, real-time video feedback is relayed from the UAV to user, thus allowing him/her to take appropriate actions. This paper presents the design implementation by discussing the various sub-systems involved, inter system communication, and field tests to ascertain operation. As presented from testing results, the proposed system provides efficient communication between the subsystems for smooth flight control, while allowing for safe transport of portable objects.