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In this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.

The presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.

The authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Before designing a navigation system, it is necessary to analyse possible approaches in terms of expected accuracy, existing limitations and economic justification to select the most advantageous solution. This paper aims to collect possible navigation methods that can provide correction for inertial navigation and to evaluate their suitability for use on a manoeuvring tactical missile.

The review of existing munitions was based on data collected from the literature and online databases. The data collected included dimensions, performance, applied navigation and guidance methods and their achievable accuracy. The requirements and limitations identified were confronted with the range of sensor parameters available on the market. Based on recent literature, navigation methods were reviewed and evaluated for applicability to inertial navigation system (INS) correction in global navigation satellite system-denied space.

The performance analysis of existing munition shows that small and relatively inexpensive micro-electro-mechanical system-type inertial sensors are required. A review of the parameters of existing devices of this type has shown that they are subject to measurement errors that do not allow them to achieve the delivery accuracy expected of precision missiles. The most promising navigation correction methods for manoeuvring flying objects have been identified.

The information presented in this paper is the result of the first phase of a project and presents the results of the requirements selection, initial sizing and preliminary design of the navigation system. This paper combines a review of the current state of the art in missile systems and an analysis of INS accuracy including the selection of sensor parameters.

Theadvanced air mobility (AAM) is defined by National Aeronautics and Space Administration (NASA) as safe, accessible, automated and affordable air transportation system for passengers and cargo, capable of serving previously hard-to-reach urban and rural sites. The purpose of this paper is to focus on explaining potential solutions, under study by the authors, which could support beyond visual line of sight (BVLOS) operations for goods delivery in a safe way.

According to recent NASA-commissioned market studies, by 2030, there will be as many as 500 million flights a year for package delivery services and 750 million flights a year for passengers’ transportation (AAM). A significant number of these aircrafts will be unmanned aerial vehicles, meaning that they are self-flying or autonomous, of which the smallest ones are quadcopters: they are relatively inexpensive and are capable to perform various tasks, such as aerial observation, crop monitoring and treatment, search and rescue, power line monitoring and goods delivery. On the other hand, there are still many difficulties in introducing them into medium- and low-risk BVLOS routine operations for goods delivery: unfortunately, there are no regulations and technologies yet that enable these operations.

This conceptual paper outlines the studies about possible solutions, identified by authors, which could support BVLOS operations in a medium- and low-risk environment; in particular, the following aspects have been analysed: regulations, integrating control systems for drones, sensors (on board obstacle detection and avoidance), emergency management (emergency on ground system to identify safe landing areas), concepts of droneway (or flight corridors) and drones recovery hub.

The purpose of this paper is to provide a conceptual description of the possible solutions, under study by the authors, which could contribute enabling the BVLOS operations in a medium- and low-risk environment. The paper aims describing the state of the art, terms of regulations, classifications and limitations and describing possible conceptual solutions that could guarantee safety in introducing unmanned aircraft system operations inside urban areas.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

This paper aims to present assessment of models and simulation results used in the development process of flight stabilisation system that uses trim tabs for PZL-130 Orlik turboprop military trainer aircraft. Flight test of the system allowed to compare software and hardware simulation results with real flight recordings.

Proposed flight stabilisation system was developed using modern techniques of model-based design, automatic code generation, software and hardware in the loop testing. The project reached flight testing stage which allowed to gather data to verify models and simulation results and asses their quality.

Results of the comparison showed that the trim tab actuator model used in simulation can be improved by adding play. This reduced the difference between simulation and real flight system output – actuator angle. The influence of airloads on the flying actuator angle compared to hardware in the loop simulation in lab is less than ± 0.6°.

Proposed flight stabilisation system that uses trim tabs has several benefits over classic automatic flight system in terms of weight, energy consumption and structure simplicity and does not need aircraft primary control modification. It was developed using modern techniques of model-based design, automatic code generation and hardware in the loop simulations.

In the aerial transportation area, fuel costs are critical to the economic viability of companies, and so urgent measures should be adopted to avoid any unnecessary increase in operational costs. In particular, this paper addresses the case of missed approach manouevres, showing that it is still possible to optimize the usual procedure.

The costs involved in a standard procedure following a missed approach are analysed through a simulation model, and they are compared with the improvements achieved with a fast reinjection scheme proposed in a prior work.

Experimental results show that, for a standard A320 aircraft, fuel savings ranging from 55% to 90% can be achieved through the reinjection method.

To the best of the authors’ knowledge, this work is the first study in the literature addressing the fuel savings benefits obtained by applying a reinjection technique for missed approach manoeuvres.

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.

By reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.

The clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.

The functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

Mobile travel apps (MTA) smart features were identified based on recent travel application (app) trends and a literature review of MTA smart features. Subsequently, the MTA features that could be prioritised to increase user interest in MTA were determined. The MTA smart feature development challenges that should be mitigated were also identified.

The app identification and selection were based on the one-stop solution characteristics containing the common function of travel apps and eight MTA smart features. A total of 193 Apple apps and 250 Google apps were identified, where 36 apps that met the inclusion and exclusion criteria based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart were selected for evaluation.

The high user ratings for apps from both app stores revealed the acceptance of smart technology in the tourism industry. Geolocation tracking services, travel itinerary generators, and real-time personalisation and recommendation were the three major features available in the included MTA. The challenges of MTA with smart features were highlighted from the tourism organisation, app developer and user perspectives.

The findings can guide tourism organisations and app developers on the smart features that MTA should offer for user engagement. Technological organisations could optimise their technology stack by considering the identified smart features. The findings are valuable for scholars in terms of MTA aesthetics and usability to gain acceptability. The development challenges included significant investment in technology, location accuracy and privacy concerns when implementing MTA smart features.

The previous literature mainly focused on evaluating app quality, assessing app functionality, and user ratings using the Mobile Application Rating Scale, and scoping reviews of MTA articles. Contrastingly, this study is among the first in which MTA smart features were examined from a developer-centric perspective. Moreover, it is suggested that MTA includes integrated smart features for better tourism services and market penetration in the tourism industry.

Post-migration stress is an important aspect of refugees’ mental health, but little is known about the relationship with insomnia. The purpose of this study was to investigate the assication between different post-migration stress factors and insomnia among Syrian refugees living in Norway.

The authors analysed survey data from the REFUGE study with 873 Syrian adults who resettled in Norway between 2015 and 2017. Potential participants were randomly selected from The Norwegian Population Register. Insomnia was measured using the Bergen Insomnia Scale. Perceived-discrimination, navigation difficulties, financial strain and loss of social status were measured via the refugee post-migration stress scale. The authors ran logistic regression analyses, controlling for socio-demographics, potentially traumatic experiences and symptoms of post-traumatic stress.

Overall, 59% (95% CI, 56%–62%) of the sample reported experiencing insomnia. In the fully-adjusted model, only financial strain and navigation difficulties were significantly associated with increased odds of insomnia. Loss of social status was also related to insomnia but only before adjusting for symptoms of post-traumatic stress.

Financial strain and navigation difficulties appear to be the most important post-migration stress factors in terms of insomnia among Syrian refugees living in Norway. Ameliorating these difficulties could potentially reduce insomnia among refugees. Since insomnia is a risk factor for the subsequent development of post-traumatic stress and depression, it is important to prevent or improve symptoms of insomnia among Syrian refugees, especially considering the high prevalence found in this study.