Integrating AADL and FMI to Extend Virtual Integration Capability

Virtual Integration Capability is paramount to perform early validation of Cyber Physical Systems. The objective is to guide the systems engineer so as to ensure that the system under design meets multiple criteria through high-fidelity simulation. In this paper, we present an integration scheme that leverages the FMI (Functional Mock-Up interface) standard and the AADL architecture description language. Their combination allows for validation of systems combining embedded platform captured by the AADL, and FMI components that represent physical elements, either mechanical parts, or the environment. We present one approach, and demonstrator case studies

Enabling Model Testing of Cyber-Physical Systems

Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system and its environment. To alleviate these challenges we propose to conduct testing at early stages and over executable models of the system and its environment. Model testing of CPSs is however not without difficulties. The complexity and heterogeneity of CPSs renders necessary the combination of different modeling formalisms to build faithful models of their different components. The execution of CPS models thus requires an execution framework supporting the co-simulation of different types of models, including models of the software (e.g., SysML), hardware (e.g., SysML or Simulink), and physical environment (e.g., Simulink). Furthermore, to enable testing in realistic conditions, the co-simulation process must be (1) fast, so that thousands of simulations can be conducted in practical time, (2) controllable, to precisely emulate the expected runtime behavior of the system and, (3) observable, by producing simulation data enabling the detection of failures. To tackle these challenges, we propose a SysML-based modeling methodology for model testing of CPSs, and an efficient SysML-Simulink co-simulation framework. Our approach was validated on a case study from the satellite domain

Systems engineering languages for modeling and analyzing supervisory control structures in cyber-physical systems

In today’s world, a new generation of high-tech cyber-physical systems are becoming an integral part of our societies and their impact is only going to increase within the next years. Because of their importance, the companies that develop these systems use proper systems engineering modeling tools to help with the design and development of these types of systems and to accelerate the whole development process. In this thesis, 4 very popular modeling tools/languages are being tested and evaluated in terms of their capabilities for model-based systems engineering. These tools are Simulink&Stateflow from MATLAB, Modelica, MechatronicUML and SysML. In order to do that, a proper introduction of the systems engineering process is presented to set the criteria in which the different tools/lan- guages will be evaluated. To support the evaluation process, a case study is presented with the CIF3 language that will be attempted with all the other languages/tools. Each modeling lan- guage/tool has been evaluated individually at first and then together with the others in the end. In addition to the first evaluation, a proper basic introduction of all the modeling concepts that each tool uses for modeling cyber-physical systems is provided and the building of the case study as well. After that, in the second evaluation, the languages are extensively compared against each other in terms of all the criteria set previously to see exactly the scope of capabilities that each tools has. As a result from the two evaluations, a definitive review for each language/tool is presented addressing their overall scope of capabilities, main strong features, main uses, possible ways of improving and future development.Outgoin

A model-based rams estimation methodology for innovative aircraft on-board systems supporting mdo applications

The reduction of aircraft operating costs is one of the most important objectives addressed by aeronautical manufactures and research centers in the last decades. In order to reach this objective, one of the current ways is to develop innovative on-board system architectures, which can bring to lower fuel and maintenance costs. The development and optimization of these new aircraft on-board systems can be addressed through a Multidisciplinary Design Optimization (MDO) approach, which involves different disciplines. One relevant discipline in this MDO problem is Reliability, Availability, Maintainability and Safety (RAMS), which allows the assessment of the reliability and safety of aircraft systems. Indeed the development of innovative systems cannot comply with only performance requirements, but also with reliability and safety constraints. Therefore, the RAMS discipline plays an important role in the development of innovative on-board systems. In the last years, different RAMS models and methods have been defined, considering both conventional and innovative architectures. However, most of them rely on a document-based approach, which makes difficult and time consuming the use of information gained through their analysis to improve system architectures. On the contrary, a model-based approach would make easier and more accessible the study of systems reliability and safety, as explained in several studies. Model Based Systems Engineering (MBSE) is an emerging approach that is mainly used for the design of complex systems. However, only a few studies propose this approach for the evaluation of system safety and reliability. The aim of this paper is therefore to propose a MBSE approach for model-based RAMS evaluations. The paper demonstrates that RAMS models can be developed to quickly and more effectively assess the reliability and safety of conventional and innovative on-board system architectures. In addition, further activities for the integration of the model-based RAMS methodology within MDO processes are described in the paper

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