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Modeling the UAV based Aircraft Preflight Inspection System Architecture and Autonomous Trajectory Tracking of a Quadrotor
Abstract
The quadrotor, a versatile aerial vehicle extensively utilized in the aerospace and
aircraft industries, plays a significant role in various applications. One crucial aspect of
aircraft operations involves visually inspecting the external surface to ensure
airworthiness and flight safety. However, manual inspection methods are prone to
errors and are time-consuming. To overcome these limitations, advanced technologies
such as commercial drones have emerged as potential solutions. Although commercial
drones are available for this purpose, meeting stakeholders' requirements within
specific contextual constraints remain challenging. Moreover, the complexity of product
design increases due to customer requirements and high expectations, necessitating
effective management strategies. To address these challenges, significant
modifications are needed for commercial drones. Model-Based Systems Engineering
(MBSE) methods have shown promise in tackling these complexities. However,
implementing MBSE successfully poses additional challenges due to a lack of proper
modeling methodologies and tools. As a result, using Arcadia as a baseline needs to
address the need for flexibility, high traceability, and well-organized interdisciplinary
interfaces.
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As mechatronic products gain in popularity, methods for mastering the complexity of these systems in development become increasingly relevant, such as model-based systems engineering (MBSE). Main pillars of MBSE are method, language and tool. A method specifies procedures in product development. The application of the method is supported by a language and tool as the language specifies a system of symbols with which development artifacts can be represented in a software environment (i.e. tool). Currently, various MBSE methods exist, such as motego. Motego specifies a framework for the function-oriented seamless development of mechatronic systems from requirements to the physical realization down to mechanical and electrical contacts and the description of these via parameters and models. Central element in MBSE is the system model, which connects all relevant development artefacts. The system model is created with a language in a software environment such as Cameo Systems Modeler. In MBSE, the graphical systems modeling language SysML is widely established. The language elements in SysML are very abstract and numerous. As a result, the language is difficult to apply. However, its reasonable applicability is an essential prerequisite for the introduction of the motego methods in industrial practice. This results in the following research need: A specific modeling language for the motego method is needed that supports its reasonable application. Therefore, in this paper a modeling language is presented whose language elements are specifically adapted to the motego method. With the help of this domain specific language, the user is guided through method-compliant modeling.
Modelling languages play a central role in developing complex, critical systems. A precise, comprehensible, and high-quality modelling language specification is essential to all stakeholders using, implementing, or extending the language. Many good practices can be found that improve the understandability or consistency of the languages’ semantics. However, designing a modelling language intended for a large audience is still challenging. In this paper, we investigate the challenges and typical issues with assessing the specifications of behavioural modelling language semantics. Our key insight is that the various stakeholder’s understandings of the language’s semantics are often misaligned, and the semantics defined in various artefacts (simulators, test suites) are inconsistent. Therefore assessment of semantics should focus on identifying and resolving these inconsistencies. To illustrate these challenges and techniques, we assessed parts of a state-of-the-art specification for a general-purpose modelling language, the Precise Semantics of UML State Machines (PSSM). We reviewed the text of the specification, analysed and executed PSSM’s conformance test suite, and categorised our experiences according to questions generally relevant to modelling languages. Finally, we made recommendations for improving the development of future modelling languages by representing the semantic domain and traces more explicitly, applying diverse test design techniques to obtain conformance test suites, and using various tools to support early-phase language design.
A robust control algorithm is always needed to harvest maximum power from a Wind Energy Conversion System (WECS) by operating it consistently at a Maximum Power Point (MPP) in the presence of wind speed variations. In this work, a Maximum Power Point Tracking (MPPT) control algorithm is designed via Conventional Sliding Mode Control (CSMC), the Super Twisting Algorithm (STA), and the Real Twisting Algorithm (RTA) and is applied to a Permanent Magnet Synchronous Generator (PMSG)-based WECS. CSMC is model-based whereas the STA and RTA are model-free controllers. In practice, the unavailability of nonlinear terms and aerodynamic forces deteriorates the performance of these controllers. Thus, an offline NeuroFuzzy algorithm is incorporated to estimate the nonlinear drift and control input channel to improve the robustness of these algorithms. In addition, the generator shaft speed and its missing derivative is recovered via a Uniform Robust Exact Differentiator (URED). In order to carry out a comprehensive comparative study among the three competitors, the overall system is simulated in a closed loop under the action of these controllers at three different operating conditions, i.e., nominal, varying load and inertia, and varying wind speed, using MATLAB/Simulink. The acquired results confirm the superiority of the RTA over the STA and CSMC in terms of robustness and chatter reduction.
A civil aircraft scaled demonstrator is an unmanned aerial vehicle (UAV) obtained by reducing the geometry of a civil aircraft by an equal scale and simplifying the airframe and airborne subsystems. Due to the low development cost and flight risk of scaled demonstrators, flight tests with demonstrators are an attractive way to assess the reliability and effectiveness of new configurations and/or technologies available for civil aircraft. Nevertheless, engineers still hope to reduce the development cycles and costs of demonstrators to evaluate their multiple civil aircraft design solutions through flight tests in a short period of time. Model-based systems engineering (MBSE) is a formalized requirement and architecture modeling method which is a useful tool in aircraft design. However, no existing MBSE framework has been found suitable for the development of demonstrators due to their unique features. In this paper, an MBSE approach for civil aircraft scaled demonstrator is proposed based on the MagicGrid methodology and the existing technical process for the demonstrator. This approach formalizes the requirement analysis and architecting for demonstrators via system modeling language (SysML). Meanwhile, the stakeholders of civil aircraft scaled demonstrators are identified, and a requirement ontology and modeling standard are established according to existing demonstrator development practice. Then, a case study is performed through a hybrid power demonstrator which carries a hydrogen fuel cell as the main power supply, a set of solar cells, and two lithium cells. The requirement traceability and verifiability are examined, and a logic simulation is executed for architecture, which shows the feasibility of applying the MBSE approach for the development of civil aircraft scaled demonstrators.
Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning.
Model Based Systems Engineering as a scientific discipline tries to address the increasing complexity of today’s cyber-physical systems by utilizing different kinds of models. In practical application, however, this approach is often constrained to SysML-based object modeling. Even though this appears to be a suitable approach for dealing with complexity, various restrictions limit stakeholder acceptance. Considering scientific discussions in the context of modeling shows two different schools of thought. On the one hand, arguments for more formalized and rigorous concepts can be found, where on the other hand, the need for more stakeholder-oriented and easier-to-understand concepts is postulated. As both are reasonable, the question of integration arises. To address this aspect, we developed the concept of Domain Specific Systems Engineering. Our research in this field lasted for nearly a decade, and different aspects have been investigated. This paper contributes a summary of the overall approach that integrates the various aspects investigated so far. Thus, the underlying concepts are explained, and the corresponding modeling stack and tool-chain are described in more detail. Further, the practical experiences from various case studies are summarized, and identified shortcomings are discussed.
A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP.
Traditional system technology modeling in conceptual aircraft design mainly relies on empirical knowledge and methods derived from conventional systems, for which valid system architecture designs are known. Since these systems have been proven valid especially from a safety perspective, detailed system safety analyses are usually not necessary. For unconventional systems and innovative technologies, on contrary, new architectures have to be designed and system safety has, therefore, to be taken into account. Therefore, the application of model-based safety assessment (MBSA) for designing system architectures in conceptual aircraft design studies is proposed. A MBSA approach based on a Simulink architecture model is presented which is tailored for use in conceptual design studies. It is applied to the cryocooling system of a hybrid-electric powertrain architecture from an already-published study. The original architecture as well as possible architecture alternatives are investigated. As a result, a safer architecture version with lower number of components can be proposed. The application example indicates that using MBSA in conceptual design benefits the latter by providing insights into safety properties of the system and by pointing out architecture safety weaknesses. This could result in safer, thus more realistic system architectures.
In the face of the rapid growth in the scale and complexity of multidisciplinary systems, being able to develop reliable systems under ever-faster changing and more individual market requirements is becoming more and more challenging. The Model-Based Systems Engineering (MBSE) approach has already been researched heavily, and started to be introduced for the management of complexity, maintaining consistency, and reducing development costs and the time-to-market. However, a major drawback of the current MBSE methodologies is the lack of capability to integrate with domain-specific simulation models to investigate design concepts in the early phases of the development process. In order to address this issue, we propose a holistic system modeling approach that allows system engineers to link descriptive system models with domain-specific simulation models. In this paper, the Systems Modeling Language (SysML) is used as the standard architecture modeling language. A system modeling approach in SysML based on the system’s functional architecture for system design and validation is defined. The approach was developed to integrate domain-specific models into the system model using a SysML modeler with the capability of running and reusing simulation tasks via the coupling of external tools, which helps to bridge the existing gap between models on the system level and detail level. The feasibility of the proposed approach will be evaluated based on the case study of a wind turbine (WT) system. The study shows that our approach has the potential to enable the consistent, parameter-based interlinkage of domain-specific models based on always-up-to-date data, and to assist engineers in making design decisions to meet the system requirements accurately and rapidly in different engineering fields.
The new progressive smart technologies announced in the fourth industrial revolution in aviation—Aviation 4.0—represent new possibilities and big challenges in aircraft maintenance processes. The main benefit of these technologies is the possibility to monitor, transfer, store, and analyze huge datasets. Based on analysis outputs, there is a possibility to improve current preventive maintenance processes and implement predictive maintenance processes. These solutions lower the downtime, save manpower, and extend the components’ lifetime; thus, the maximum effectivity and safety is achieved. The article deals with the possible implementation of an unmanned aerial vehicle (UAV) with an infrared camera and Radio Frequency Identification (RFID) as two of the smart hangar technologies for airframe condition monitoring. The presented implementations of smart technologies follow up the specific results of a case study focused on trainer aircraft failure monitoring and its impact on maintenance strategy changes. The case study failure indexes show the critical parts of aircraft that are subjected to damage the most. The aim of the article was to justify the need for thorough monitoring of critical parts of the aircraft and then analyze and propose a more effective and the most suitable form of technical condition monitoring of aircraft critical parts. The article describes the whole process of visual inspection performed by an unmanned aerial vehicle (UAV) with an IR camera and its related processes; in addition, it covers the possible usage of RFID tags as a labeling tool supporting the visual inspection. The implementations criteria apply to the repair and overhaul small aircraft maintenance organization, and later, it can also increase operational efficiency. The final suggestions describe the possible usage of proposed solutions, their main benefits, and also the limitations of their implementations in maintenance of trainer aircraft.
This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure (BHP) estimation. The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling. For the two case studies, measured field data of the wellbore filled with gasified mud system was utilized, and the wellbores were drilled using rotary jointed drill strings. Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy, BHP from measured field data. For modeling purpose, an extensive data from six fields was used, and the proposed model was further validated with two data from two new fields. The gathered data encompasses a variety of well data, general information/data, depths, hole size, and depths. The developed model was compared with data obtained from two new fields based on its capability, stability and accuracy. The result and model's performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R² greater than 0.9. The high values of R² for the two models suggest the relative reliability of the modelling techniques. The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%–2.14%, for the Extra tree model and 0.40–0.41 and 3.90%–3.99% for Feed Forward model respectively; the least errors were recorded for the Extra Tree model. Also, the mean absolute error of the Extra Tree model for both fields (9.13–10.39 psi) are lower than that of the Feed Forward model (10.98–11 psi), thus showing the higher precision of the Extra Tree model relative to the Feed Forward model. Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well's extension ability, because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point. Thus, the application of this study proposed models for predicting bottomhole pressure trends.
The internal and external interactions between the complex structural and behavioral characteristics of the system of interest and the surrounding environment result in unpredictable emergent behaviors. These emergent behaviors are not well understood, especially when modeled using the traditional top-down systems engineering approach. The intrinsic nature of current complex systems has called for an elegant solution that provides an integrated framework in Model-Based Systems Engineering. A considerable gap exists to integrate system engineering activities and engineering analysis, which results in high risk and cost. This thesis presents a framework that incorporates indefinite and definite modeling aspects that are developed to determine the complexity that arises during the development phases of the system. This framework provides a workflow for modeling complex systems using Systems Modeling Language (SysML) that captures the system’s requirements, behavior, structure, and analytical aspects at both problem definition and solution levels. This research introduces a new level/dimension to the framework to support engineering analysis integrated with the system architecture model using FMI standards. A workflow is provided that provides the enabling methodological capabilities. It starts with a statement of need and ends with system requirement verification. Detailed traceability is established that glues system engineering and engineering analysis together. Besides, a method is proposed for predicting the system’s complexity by calculating the complexity index that can be used to assess the complexity of the existing system and guide the design and development of a new system. To test and demonstrate this framework, a case study consisting of a complex district cooling system is implemented. The case study shows the framework’s capabilities in enabling the successful modeling of a complex district cooling system. The system architecture model was developed using SysML and the engineering analysis model using Modelica. The proposed framework supports system requirements verification activity. The analysis results show that the district chiller model developed using Modelica produces chilled water below 6.6 degrees Celsius, which satisfies the system requirement for the district chiller system captured in the SysML tool. Similarly, many such requirement verification capabilities using dynamic simulation integration with the high-level model provides the ability to perform continuous analysis and simulation during the system development process. The systems architecture complexity index is measured for the district cooling case study from the black-box and white box-perspective. The measured complexity index showed that the system architecture’s behavioral aspect increases exponentially compared to the structural aspect. The systems architecture’s complexity index at black-box and white-box was 4.998 and 67.3927, respectively.
Nowadays, it is very important for the success of the determined missions or operations that the Unmanned Aerial Vehicles (UAVs), which are used extensively in the performance of many civil and military tasks, follow the predetermined path with high accuracy at the determined altitude. The fact that the UAV performs its mission by adhering to the predetermined height and path enables the UAV to spend less energy and therefore fly for a longer time. Many traditional control algorithms, especially Proportional-Integral-Derivative (PID), are used in the attitude and altitude control of UAV for path following. Unlike other studies, in this study, meta-heuristic optimization algorithms based on swarm intelligence estimate the parameters of the control algorithm proposed for UAV. Using meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO), both attitude and altitude control of the quadrotor have been performed for path following in routes with different geometries such as rectangle, circle, and lemniscate. The performance of each control algorithm in the study for the specified routes has been tested and the test results obtained have been compared with each other. Considering the features such as simplicity, flexibility, ability to search randomly and avoiding local optima, a new control algorithm whose , and parameter values optimized by HHO, have been proposed for UAV's attitude and altitude control.
Fog computing is becoming a popular paradigm for bringing the advantages of the cloud nearer to the network edge. This way, computational tasks can be offloaded from end devices to nearby fog nodes, thus benefiting from high computational power and low latency at the same time. Architecture plays a central role in fog computing. Many papers on fog computing address architectural questions. However, a closer look reveals that different papers use the term “architecture” for very different concepts. This is rooted in the multi-disciplinary nature of the fog computing paradigm. The different communities involved in fog computing—network, hardware, system software, application software—all use the term “architecture,” but with different meaning. To facilitate the mutual understanding of architectural issues in fog computing, this paper introduces a conceptual framework for reasoning about architecture in fog computing. This conceptual framework uses three independent dimensions to describe architecture. Based on the three architecture dimensions, several architecture views can be defined to serve the different viewpoints of the involved disciplines, and to highlight different aspects of the architecture. The conceptual framework is validated using a literature mapping study.
This chapter introduces the Internet of Things (IoT) and presents definitions and a general framework for conceptualising IoT. Key concepts and enabling technologies are summarised followed by a synthesis and discussion of the current state-of-the-art in IoT Reference Architectures.
Architecture definition, which is central to system design, is one of the two most used technical processes in the practice of model-based systems engineering. In this article, a fundamental approach to architecture definition is presented and demonstrated. The success of its application to engineering problems depends on a precise but practical definition of the term architecture. In the standard for architecture description, ISO/IEC/IEEE 42010:2011, a definition was adopted that has been subsumed into later standards. In 2018, the working group JTC1/SC7/WG42 on system architecture began a review of the adopted definition, holding sessions late in the year. This article extends and complements a position paper submitted during the meetings, in which Tarski model theory and ISO/IEC 24707:2018 (logic-based languages) were used to better understand relationships between system models and concepts related to architecture. Independent from the working group, it now contributes intuitive fundamental definitions of the terms architecture and system that are used to specify a mathematically based technical process for architecture definition. The engineering utility and benefits to complex system design are demonstrated in a diesel engine emissions reduction case study.
Mastering the complexity of safety assurance for modern, software-intensive systems is challenging in several domains, such as automotive, robotics, and avionics. Model-based safety analysis techniques show promising results to handle this challenge by automating the generation of required artifacts for an assurance case. In this work, we adapt prominent approaches and propose to augment of SysML models with component fault trees (CFTs) to support the fault tree analysis and the failure mode and effects analysis. While most existing approaches based on CFTs are only targeting the system topology, e. g., UML class diagrams, we propose an integration of CFTs with SysML internal block diagrams as well as SysML activity diagrams. We realized our approach in a prototypical tool. We conclude with best practices and lessons learned that emerged from our case studies with an electronic power steering system and a boost recuperation system.
The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.
A key challenge faced by biomimicry practitioners is making the conceptual leap between biology and design, particularly regarding collaborating across these knowledge domains and developing and evaluating design principles abstracted from biology. While many tools and resources to support biomimicry design exist, most largely rely on semantic techniques supporting analogical translation of information between biology and design. However, the challenges of evaluation and collaboration are common in design practice and frequently addressed through prototyping. This study explores the utility of prototyping in the unique context of biomimicry by investigating its impact on the abstraction and transfer of design principles derived from biology as well as on cross-domain collaboration between biologists and designers. Following a survey exploring current practices of practitioners, in depth interviews provided detailed accounts of project experiences that leveraged prototyping. Four primary themes were observed: (1) Approximation; (2) The Prototyping Principle; (3) Synthesis and Testing; and (4) Validation. These themes introduce a unique abstraction and transfer process based on form-finding and collaborative performance evaluation in contrast to the widely accepted semantic language-based approaches. Our findings illustrate how designers and engineers can leverage a prototyping skillset in order to develop boundary objects between the fields of biology and design to navigate challenges uniquely associated with the biomimicry approach.
In the tide of smart manufacturing in the world, many countries have put forward their own reference frameworks for smart manufacturing system. Based on the framework of smart manufacturing system (SMS) proposed by China, a reference smart manufacturing integration system (SMIS) from multi-level and multi-perspective was proposed. The reference model of SMIS was analyzed by means of system engineering. Based on this model, the overall architecture of SMIS was proposed. Then, the physics subsystem, information integration subsystem, network integration subsystem, data integration subsystem, and visualization integration subsystem for SMIS were proposed through the overall architecture of SMIS. Finally, the above integrated subsystems were collected together to deduce the implementation path of SMIS. Four reference subsystems for SMIS proposed in this paper have achieved good effects in the projects we participated in, which were organized by the state. The research results of this paper can be used as a reference for industry and government to design, set, and carry out SMIS. At the same time, it has a certain reference value for the improvement and supplement of national smart manufacturing system architecture.
A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-Degree of Freedom (DOF) unmanned aerial vehicles (UAV) quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system is obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using Genetic Algorithm (GA) to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.
Achieving a better separation of concerns has been the preoccupation of the software engineering research community. The main goal includes the attainment of modularity and reasoning about software systems amongst others. Many paradigms (aspect oriented programming inclusive) had been proposed to actualize the separation of concerns. Aspect Oriented Programming is characterized by its twin attributes of obliviousness and quantification. It also solves the twin problems of scattering and tangling of codes. However, these attributes according to a section of the research community, sacrifice program understanding and modularity. In this paper we present the Cross Cutter design pattern, based on the existing object-oriented programming technology, as an approach at separating the concerns that crosscut software systems. The pattern was implemented on a distributed system. The two approaches were evaluated using standard paradigm-independent metrics. The metrics used are the separation of concern (SoC) metrics of Concern Diffusion over Components (CDC), Concern Diffusion over Operations (CDO), and Concern Diffusion over Length of Codes (CDLOC). The results obtained suggest that CCDP offers a better separation of concern than the Aspect-Oriented Programming paradigm. The pattern preserves the notion of modularity and a concise way of reasoning about the software amongst other attributes.
Precision trajectory tracking problem for Unmanned Aerial Systems (UAS) is addressed in this work. A novel algorithm that combines a Nonsingular Modified Super-Twisting Controller with a High Order Sliding Mode Observer to enable an aerial vehicle tracking a desired trajectory under the assumption that i) its translational velocities are not available and ii) there are unmodeled dynamics and external disturbances. The proposed Sliding Mode Controller is based on a nonlinear sliding mode surface that ensures that the position and velocity tracking errors of all system’s state variables converge to zero in finite time. Moreover, the proposed controller generates a continuous control signal eliminating the chattering phenomenon. Finally, simulation results and an extensive set of experiments are presented in order to illustrate the robustness and effectiveness of the proposed control strategy.
Microservices is an architectural style increasing in popularity. However, there is still a lack of understanding how to adopt a microservice-based architectural style. We aim at characterizing different microservice architectural style patterns and the principles that guide their definition. We conducted a systematic mapping study in order to identify reported usage of microservices and based on these use cases extract common patterns and principles. We present two key contributions. Firstly, we identified several agreed microservice architecture patterns that seem widely adopted and reported in the case studies identified. Secondly, we presented these as a catalogue in a common template format including a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies. We can conclude that different architecture patterns emerge for different migration, orchestration, storage and deployment settings for a set of agreed principles.
This paper presents a comparative analysis of two non-linear estimation techniques namely high-gain observer (HGO) and super-twisting sliding mode observer (STSMO) when applied for output feedback model-based control of the Quad-copter. Under the restriction that absolute position w.r.t initial frame and attitude angles are available while remaining velocities are estimated on-line. Adaptive Sliding mode control was developed and implemented as a control scheme for the closed-loop system to handle not only the aerodynamic parameter uncertainties, external disturbances and modelling inaccuracies but also to improve performance and reliability of the Quad-copter flight. The performance of the non-linear observers are evaluated in terms of their closed-loop behaviour, ability to cope with model imperfections and process uncertainties such as measurement errors and uncertain initial conditions. Based on the simulations results a performance comparison of both observers is made with adaptive sliding mode control as control scheme.
One of the primary hazards associated with the operation of Unmanned Aircraft (UA) is the controlled or uncontrolled impact of the UA with terrain or objects on the terrain (e.g., people or structures). National Aviation Authorities (NAAs) have the responsibility of ensuring that the risks associated with this hazard are managed to an acceptable level. The NAA can mandate a range of technical (e.g., design standards) and operational (e.g., restrictions on flight) regulatory requirements. However, work to develop these regulations for UA is ongoing. Underpinning this rule-making process is a safety case showing how the regulatory requirements put in place ensure that the UA operation is acceptably safe for the given application and environment. There is no accepted framework for structuring or assessing the safety case for UA operations over populous areas, or for assessing the effectiveness of the wide range of potential technical and operational measures that can be potentially employed as part of a safety case. This paper presents a Barrier Bow Tie (BBT) model as a suitable template for the development and assessment of safety cases for UA operations over populous areas. The components of the BBT model and its application to UA operations over populous areas are presented. A case study is used to highlight the desirable features of the BBT model. The BBT model provides a systematic means for classifying and assessing proposed risk controls (the points of regulation) and how these controls contribute (in isolation and in combination) towards a reduction in risk.
This chapter will chronicle the journey of the community of operators, regulators, and researchers who embarked upon the task of identifying critical human factors of maintenance and inspection, establishing a database and research tools, developing practical strategies for reducing the risks of maintenance and inspection errors, and understanding its critical role in preserving flight safety. We will take a historical perspective, developing a timeline that is largely driven by key events such as accidents and regulatory/government actions and initiatives. Part 1 focuses on how the field of maintenance human factors was built, beginning in the late 1980s. Part 2 describes the development of methods and tools for meeting the maintenance human factors challenges identified. Finally, Part 3 updates the original chapter by focusing on issues that have persisted or emerged since 2010.
This study compares the types and quantities of knowledge that are captured by a model‐based systems engineering (MBSE) approach and a traditional architecting approach to measure the benefits of the MBSE approach in managing the complexity of a robotic space system. The MBSE approach was implemented with Cameo Systems Modeler using Systems Modeling Language (SysML) and applied to architecting an orbiting sample Capture and Orient Module (COM) system concept for a Capture, Containment, and Return System payload concept for potential Mars Sample Return. An architecture framework was established, covering system, subsystem, and assembly levels, along with structure, behavior, data, and requirements perspectives. The COM system architecture was captured in parallel using both the MBSE and non‐MBSE approaches in order to provide a side‐by‐side comparison of the approaches. The approaches were evaluated based on how well each represented the information content of the COM system architecture. A total of 4389 knowledge elements were classified using the Revised Bloom's Taxonomy knowledge dimension and used to quantitatively compare the two approaches. The MBSE approach more completely captured architectural knowledge than the non‐MBSE approach. Limitations to the SysML‐based MBSE approach were also identified, including its ability to fully represent certain high‐level conceptual, procedural, and metacognitive knowledge such as design principles, design approaches and rationales, risks, development strategies and rationales, organizational core competencies, and requirement verification methods. The overall results demonstrate the benefits of MBSE in managing the complexity of robotic space systems and strengthen the case for adopting MBSE within the systems engineering community.
Traditional document-based practices in systems engineering are being transitioned to model-based ones. Adoption of model-based systems engineering (MBSE) continues to grow in industry and government, and MBSE continues to be a major research theme in the systems engineering community. In fact, MBSE remains a central element in the International Council on Systems Engineering (INCOSE)'s vision for 2025. Examining systems engineering literature, this paper presents an assessment of the extent to which benefits and value of MBSE are supported by empirical evidence. A systematic review of research and practice papers in major systems engineering archival journals and conference proceedings was conducted. Evidence was categorized in four types, two of which inductively emerged from the results: measured, observed (without a formal measurement process), perceived (claimed without evidence), and backed by other references. Results indicate that two thirds of claimed MBSE benefits are only supported by perceived evidence, while only two papers reported measured evidence. The aggregate assessment presented in this paper indicates that claims about the value and benefits of MBSE are mainly based on expectation. We argue that evidence supporting the value and benefits of MBSE remains inconclusive.
Deep learning (DL) is adapted in many areas of artificial intelligence such as speech recognition, image recognition, natural language processing, robot navigation systems, and self-driving cars. This field also aids in analyzing the spread of dangerous diseases such as in the case of pandemics with many social behavior and the other environmental factor, modeling condition diseases such as obesity, and tracking public health. In addition to the aforementioned, there are several categories of tools that help deep learning engineers to do work faster and more effectively. There is much class of tools that enable deep learning engineers to actually do their work faster and more effectively. Some of the tools include TensorFlow, Keras, Caffe, Torch, and so on. DL models make use of several kinds of advanced algorithms. Some algorithms are best suited to perform specific tasks. To choose the right ones, it is good to gain grasp of all primary algorithms. Excellent knowledge of advanced DL techniques, their types, and applications can help users execute it for various purposes.
The physical world is transformed into being digitized and makes everything connected. An explosion of smart devices and technologies has allowed mankind to be in constant communication anywhere and anytime. IoT trend has created a sub-segment of the IoT market known as the industrial Internet of Things (IIoT) or Industry 4.0. Industry 4.0 dubbed I4.0 marks the fourth in the Industrial Revolution that focuses heavily on interconnectivity, automation, autonomy, machine learning, and real-time data. By 2020, it is estimated that over 30 billion of the world's devices will be connected in some way—which is 20 billion more devices than today! The consistent capturing and transmitting of data among machines provide manufacturing companies with many growth opportunities. The IIoT is expected to transform how we live, work and play. The number one challenge faced by the Industrial IoT is security and privacy. If we cannot alleviate many of the security and privacy issues that impact the Industrial IoT, we will not be able to achieve its full potential. IoT and the trend toward greater connectivity means more data gathered from more places, in real time, to enable real-time decisions and increase revenue, productivity, and efficiency.
Researchers have been extensively exploring the employment of generative systems to support design practices in the architecture, engineering and construction industry since the 1970s. More than half a century passed since the first architecture, engineering and construction industry’s generative systems were developed; researchers have achieved remarkable leaps backed by advances in computing power and algorithms’ capacity. In this article, we present a systematic analysis of the literature published between 2009 and 2019 on the utilization of generative systems in the design practices of the architecture, engineering and construction industry. The present research studies present trends, collaborations and applications of generative systems in the architecture, engineering and construction industry in order to identify existing shortcomings and potential advancements that balance the need for theory development and practical application. It provides insightful observations that are translated into meaningful recommendations for future research necessary to progress the incorporation of generative systems into the design practices of the architecture, engineering and construction industry.
Context
Software architecture viewpoints modularize the software architectures in terms of different viewpoints that each address a different concern. Unified Modeling Language (UML) is so popular among practitioners for modeling software architectures from different viewpoints.
Objective
In this paper, we aimed at understanding the practitioners’ UML usage for the modeling of software architectures from different viewpoints.
Method
To this end, 109 practitioners with diverse profiles have been surveyed to understand practitioners’ UML usage for six different viewpoints: functional, information, concurrency, development, deployment, and operational. Each viewpoint has been considered in terms of a set of software models that can be created in that viewpoint.
Results
The survey includes 35 questions for different viewpoint models, and the results lead to interesting findings. While the top popular viewpoints for the UML-based software architecture modeling are the functional (96%) and information (99%) viewpoints, the least popular one is the operational viewpoint that is considered by 26% of the practitioners. The top popular UML modeling tool is Enterprise Architect regardless of the viewpoints considered. Concerning the software models that can be created in each viewpoint, UML’s class diagram is practitioners’ top choice for the functional structure (71%), data structure (85%), concurrency structure (75%), software code structure (34%), and system installation (39%), and system support (16%) models; UML’s sequence diagram is the top choice for the data lifecycle models (47%); UML’s deployment diagram for the physical structure (71%), mapping between the functional and physical components (53%), and system migration (21%) models; UML’s activity diagram for the data flow (65%), software build and release processes (20–22%), and system administration (36%) models; UML’s component diagram for the mapping between the functional and concurrent components (35%), software module structure (47%), and system configuration (21%) models; and UML’s package diagram for the software module structure (47%) models.
Modularity is one of the most useful tools employed in the product development process. Regarding functionality, the use of modules is common to generate flexible platforms to manufacture products and product families that require functional variations. In the current globalized market, the mass individualization or personalization is the preferred production model that delivers cost-effectiveness and satisfaction at the level of the market of one. In this model, the modularity is employed as a powerful concept applied not only for the manufacture but also for the use and final disposal stages, in which the design of modules provides functionalities and features that satisfy a variety of specifications for different market segments. Despite the existence of approaches in modularity and its usefulness in product development, it is possible to identify a lack of analysis of modular and open architecture to enhance the sustainability performance of products regarding strategies to diminish adverse impacts during their lifecycle. This paper provides an analysis of the influence and potential of Modular Architecture Principles – MAPs in the sustainable design of open architecture products. Additionally, lifecycle considerations are analysed to identify and propose strategies that enforce the sustainability performance of products concerning personalization from early design stages
This paper presents a nonlinear control of a quadrotor unmanned aerial vehicle(UAV) for trajectory tracking. The dynamical model is obtained by the Euler- Lagrange methodology. In this paper, the proposed control strategy is based on the integral backstepping technique with sliding mode control (SMC) for altitude and lateral motion. In addition, an inner loop control is used to stabilize the vehicle orientation. The implementation is applied to the Qball-X4 prototype of Quanser Inc. which has OptiTrackTM cameras to provide the vehicle lateral position and a sonar sensor gives the altitude measurement. The experimental test results illustrate the effectiveness on the quadrotor of the proposed control scheme.
Not only do modeling and simulation help provide a better understanding of how real-world systems function, they also enable us to predict system behavior before a system is actually built and analyze systems accurately under varying operating conditions. Modeling and Simulation of Systems Using MATLAB® and Simulink® provides comprehensive, state-of-the-art coverage of all the important aspects of modeling and simulating both physical and conceptual systems. Various real-life examples show how simulation plays a key role in understanding real-world systems. The author also explains how to effectively use MATLAB and Simulink software to successfully apply the modeling and simulation techniques presented. After introducing the underlying philosophy of systems, the book offers step-by-step procedures for modeling different types of systems using modeling techniques, such as the graph-theoretic approach, interpretive structural modeling, and system dynamics modeling. It then explores how simulation evolved from pre-computer days into the current science of today. The text also presents modern soft computing techniques, including artificial neural networks, fuzzy systems, and genetic algorithms, for modeling and simulating complex and nonlinear systems. The final chapter addresses discrete systems modeling. Preparing both undergraduate and graduate students for advanced modeling and simulation courses, this text helps them carry out effective simulation studies. In addition, graduate students should be able to comprehend and conduct simulation research after completing this book.
While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network.
Expert and intelligent control schemes have recently emerged out as a promising solution with robustness which can efficiently deal with the nonlinearities, along with various types of modelling uncertainties, present in different real world systems e.g. binary distillation column. This paper is an attempt to propose an intelligent control system which takes the form of a fractional order fuzzy proportional–integral–derivative (FOFPID) controller which is investigated as a solution to deal with the complex dynamic nature of the distillation column. The FOFPID controller is an extension of an existing formula based self tuning fuzzy proportional integral controller structure, which varies its gains at run time in accordance with the instantaneous error and rate of change of error. The FOFPID controller is a Takagi–Sugeno (TS) model based fuzzy adaptive controller comprising of non-integer order of integration and differentiation operators used in the controller. It has been observed that inclusion of non-integer order of the integration and differentiation operators made the controller scheme more robust. For the performance evaluation of the proposed scheme, the performance of FOFPID controller is compared with that of its integer order counterpart, a fuzzy proportional–integral–derivative (FPID) controller. The parameters of both the controllers were optimized for minimum integral of absolute error (IAE) using a bio-inspired global optimization algorithm, genetic algorithm (GA). Intensive LabVIEWۛ simulation studies were performed which included setpoint tracking with and without uncertainties, disturbance rejection, and noise suppression investigations. For testing the parameter uncertainty handling capability of the proposed controller, uncertain and time varying relative volatility and uncertain tray hydraulic constant were applied. Also, for the disturbance rejection studies, intensive simulations were conducted, which included two most common causes of disturbance i.e. variation in feed composition and variation in feed flow rate. All the simulation investigations clearly suggested that FOFPID controller provided superior performance over FPID controller for each case study i.e. setpoint tracking, disturbance rejection, noise suppression and parameter uncertainties.
1 Reference Frames This section describes the various reference frames and coordinate systems that are used to describe the position of orientation of aircraft, and the transformation between these coordinate systems. It is necessary to use several different coordi-nate systems for the following reasons: • Newton's equations of motion are given the coordinate frame attached to the quadrotor. • Aerodynamics forces and torques are applied in the body frame. • On-board sensors like accelerometers and rate gyros measure information with respect to the body frame. Alternatively, GPS measures position, ground speed, and course angle with respect to the inertial frame. • Most mission requirements like loiter points and flight trajectories, are spec-ified in the inertial frame. In addition, map information is also given in an inertial frame. One coordinate frame is transformed into another through two basic opera-tions: rotations and translations. Section 1.1 develops describes rotation matrices and their use in transforming between coordinate frames. Section 1.2 describes the specific coordinate frames used for micro air vehicle systems. In Section 1.3 we derive the Coriolis formula which is the basis for transformations between between between translating and rotating frames.