SysML-based Profile for Dependable UAV Design

Steurer, Mikael; Morozov, Andrey; Janschek, Klaus; Neitzke, Klaus-Peter

doi:10.1016/j.ifacol.2018.09.722

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…Also, structural, behavioral, and requirement characteristics were considered when designing the architecture of UAVs. In Steurer, Morozov [9] study, they analyzed the UAV dependability profile using SysML. Dual-graph error propagation model (DEPM) was part of their methodology, while a method for converting SysML models to DEPM was developed.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

Hossain

Lutfi

Ahmed

et al. 2022

Systems

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The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both research and commercial purposes. In spite of their recent popularity; there remain a number of difficulties in the design representation of UAVs, including low image analysis, high cost, and time consumption. In addition, it is challenging to represent systems of systems that require multiple UAVs to work in cooperation, sharing resources, and complementing other assets on the ground or in the air. As a means of compensating for these difficulties; in this study; we use a model-based systems engineering (MBSE) approach, in which standardized diagrams are used to model and design different systems and subsystems of UAVs. SysML is widely used to support the design and analysis of many different kinds of systems and ensures consistency between the design of the system and its documentation through the use of an object-oriented model. In addition, SysML supports the modeling of both hardware and software, which will ease the representation of both the system’s architecture and flow of information. The following paper will follow the Magic Grid methodology to model a UAV system across the SysML four pillars and integration of SysML model with external script-based simulation tools, namely, MATLAB and OpenMDAO. These pillars are expressed within standard diagram views to describe the structural, behavior, requirements, and parametric aspect of the UAV. Finally, the paper will demonstrate how to utilize the simulation capability of the SysML model to verify a functional requirement.

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Section: Related Workmentioning

confidence: 99%

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

Hossain

Lutfi

Ahmed

et al. 2022

Systems

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“…In [40] Steurer et al present a SysML profile, i.e. a customization of the SysML language, termed as 'UDP', an acronym for 'UAV Dependability Profile.'…”

Section: A Sysml and Uavsmentioning

confidence: 99%

An Educational Case Study of Using SysML and TTool for Unmanned Aerial Vehicles Design

Apvrille

Saqui-Sannes

Vingerhoeds

2020

IEEE J. Miniat. Air Space Syst.

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The paper shares an experience in using the Systems Modeling Language (SysML) for the design and formal verification of UAVs. In particular, the paper shows how our approach helps detecting early design errors. A UAV in charge of taking pictures serves as educational and running example throughout the paper. The SysML model of the UAV is simulated and formally verified using the free and open-source tool named TTool. This educational case study gives the authors of the paper an opportunity to draw lessons from teaching SysML.

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“…The DEPM allows the computation of the reliability metrics using underlying Discrete-time Markov chain (DTMC) models. DEPMs can be automatically generated from common CPS models including the Simulink/Stateflow models [19], UML models [20], SysML models [21], AADL [22] models, as well as software source code using LLVM [23]. This allows not only the automated application of our method, but also the analysis of systems developed with a combination of modeling paradigms.…”

Section: Dual-graph Error Propagation Modelmentioning

confidence: 99%

“…The tool also integrates optimization methods against state space explosion of DTMC models such as the automatic nesting algorithm and data flow slicing [25]. The model transformation algorithms allow automatic generation of DEPMs from the baseline system models UML/SysML [21], Simulink/Stateflow [19], AADL [22]. After that OpenErrorPro automatically creates discrete time Markov chain models using the DEPM representation and computes required numerical reliability properties using PRISM software.…”

Section: Dual-graph Error Propagation Modelmentioning

confidence: 99%

“…The DEPM is a powerful analytical instrument for the modeling of a broad set of dependability-related features that influence error propagation processes. The feasibility of the DEPM approach has been witnessed by a number of case studies including navigation system of a mobile robot [27], automated medical patient table motion control [22], robotic software for space-to ground haptic feedback control [18], and embedded flight control software of a UAV [21]. DEPM framework could be applied for CPS in the analysis of the propagation of typical CPS errors from potentially faulty components to technically accessible system variables, e.g.…”

Section: Model-based Stochastic Epa For Cyber-physical Systemsmentioning

confidence: 99%

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Model-based Stochastic Error Propagation Analysis for Cyber-Physical Systems

Fabarisov¹,

Yusupova²,

Ding³

et al. 2020

ACTA POLYTECH HUNG

Self Cite

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Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies that is focusing on the creation of smart factories with the modular structured Cyber-Physical Systems (CPS), in tight cooperation with humans. This trend also implies that the systems become more complex, heterogeneous, and distributed especially their network and software parts. This makes the CPS highly critical subject to failures at different levels, including software, hardware, and human operators. Consequently, ensuring reliable and safe operation under the presence of non-avoidable threats also becomes a more complicated task. The proper analysis of the CPS requires thorough comprehension of both the dependability properties of system components and their interactions as well as structural and behavioral aspects of the complete system. Such an analysis of complex and mutually interlinked system properties puts considerable challenges on appropriate methods for modeling and analysis, as well as, on the related applied software tools. The Dual-graph Error Propagation Model (DEPM), developed in our lab, is a mathematical abstraction of the main future system's properties, which are vital for the determination of the error propagation processes. It is a useful analytical instrument for the evaluation of the influence of particular faults and errors to the overall system behavior. OpenErrorPro is our analytical software tool for stochastic error propagation analysis that supports the DEPM framework. Using OpenErrorPro, a DTMC model could be automatically generated from a DEPM, and the reliability metrics, in addition to, error propagation path, can be computed. This could be implemented for the analysis of the heterogeneous CPS components. The necessary steps for the DEPM framework extension, required for such an implementation, are discussed in this paper.

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SysML-based Profile for Dependable UAV Design

Cited by 14 publications

References 9 publications

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

An Educational Case Study of Using SysML and TTool for Unmanned Aerial Vehicles Design

Model-based Stochastic Error Propagation Analysis for Cyber-Physical Systems

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