A Model-Based Systems Architecting and Integration Approach Using Interlevel and Intralevel Dependency Matrix

We introduce the Concept→Model→Graph→View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

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Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

Journal of Navigation ◽

10.1017/s0373463321000424 ◽

2021 ◽

pp. 1-14

Author(s):

Hery A. Mwenegoha ◽

Terry Moore ◽

James Pinchin ◽

Mark Jabbal

Keyword(s):

Unmanned Aerial Vehicles ◽

Measurement Unit ◽

Parameter Uncertainties ◽

Navigation Solution ◽

Model Based ◽

Error Characteristics ◽

Aerial Vehicles ◽

Integration Approach ◽

Integration Architecture ◽

Model Control

Abstract The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach.

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Category-Theoretic Formulation of Model-Based Systems Architecting: The Concept-Model-Graph-View-Concept Transformation Cycle

10.20944/preprints202102.0316.v1 ◽

2021 ◽

Author(s):

Yaniv Mordecai ◽

James Fairbanks ◽

Edward Crawley

Keyword(s):

Systems Engineering ◽

System Architecture ◽

Mathematical Framework ◽

Conceptual System ◽

Architecture Model ◽

Concept Model ◽

Model Based ◽

Systems Architecting ◽

Model Graph ◽

Transformation Cycle

We introduce the Concept-Model-Graph-View-Concept (CMGVC) transformation cycle. The CMGVC cycle facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a common, robust graph data structure (GDS). The GDS is then transformed into multiple views on the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC cycle to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a decision support matrix (DSM) that stimulates system architecture insight.

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