Model-Based Systems Engineering and Control System Development via Virtual Hardware-in-the-Loop Simulation

2010 ◽  
Author(s):  
Lawrence Michaels ◽  
Sylvain Pagerit ◽  
Aymeric Rousseau ◽  
Phillip Sharer ◽  
Shane Halbach ◽  
...  
Keyword(s):  
Control System ◽  
Systems Engineering ◽  
System Development ◽  
Hardware In The Loop ◽  
Model Based ◽  
And Control ◽  
Model Based Systems Engineering

scholarly journals Microgrid System Design Based on Model Based Systems Engineering: the Case Study in the Amazon Region

2021 ◽  
Author(s):  
Miguel Angel Orellana Postigo ◽  
José Reinaldo Silva
Keyword(s):  
Systems Engineering ◽  
Amazon Forest ◽  
Small Communities ◽  
Model Based ◽  
Network Operation ◽  
Engineering Effort ◽  
Definition Of ◽  
And Control ◽  
Model Based Systems Engineering

Microgrid is a technically and economically viable opportunity to meet the demands of populations that, for various reasons, do not have access to electricity. The complexity of Smart Grid (SG) systems requires considerable engineering effort in the design process. Designing this type of complex system requires new approaches, methods, concepts and engineering tools. Where, requirements analysis plays a major role in better characterizing, understanding and specifying the domain of application that SG systems should solve. This work presents a systemic proposal based specifically on System Systems (SoS) which anticipates the formalization of requirements, aiming to understand, analyze and design SG within the scope of Model Based Systems Engineering (MBSE). The definition of a microgrid from the SoS perspective is presented in order to provide a complete view of its life cycle. Requirements would be represented in an Objective Oriented  Requirements Engineering (GORE) approach, specifically using visual diagrams based on the Keep All  Objectives Satisfied (KAOS) method, where network operation and control will be formally represented. A case  study for small communities in the equatorial Amazon forest is used as a case study for the proposed method.

scholarly journals Model-Based Systems Engineering Approach with SysML for an Automatic Flight Control System

2021 ◽  
Author(s):  
Haluk Altay ◽  
M. Furkan Solmazgül
Keyword(s):  
Control System ◽  
Complex Systems ◽  
Systems Engineering ◽  
Flight Control ◽  
Flight Control System ◽  
Engineering Approach ◽  
Model Based ◽  
Multidisciplinary Study ◽  
Model Based Systems Engineering

Systems engineering is the most important branch of engineering in interdisciplinary study. Successfully performing a multidisciplinary complex system is one of the most challenging tasks of systems engineering. Multidisciplinary study brings problems such as defining complex systems, ensuring communication between stakeholders, and common language among different design teams. In solving such problems, traditional systems engineering approach cannot provide an efficient solution. In this paper, a model-based systems engineering approach is applied with a case study and the approach is found to be more efficient. In the case study, the design of the helicopter automatic flight control system was realized by applying model-based design processes with integration of tools. Requirement management, system architecture management and model-based systems engineering processes are explained and applied of the case study. Finally, model-based systems engineering approach is proven to be effective compared with the traditional systems engineering methods for complex systems in aviation and defence industries.

Detecting Occurrences of the “Substitution Myth”: A Systems Engineering Template for Modeling the Supervision of Automation

2016 ◽  
Vol 11 (2) ◽  
pp. 184-199
Author(s):  
Patrick Chisan Hew
Keyword(s):  
Human Factors ◽  
Systems Engineering ◽  
Command And Control ◽  
Electronic Warfare ◽  
Weapon Systems ◽  
Model Based ◽  
And Control ◽  
Self Protection ◽  
Model Based Systems Engineering

Current usages of model-based systems engineering allow naïve substitutions of humans by machines. Human factors / ergonomics researchers have rejected such substitutions as the “substitution myth,” for if work is reallocated from a human to a machine, then there is work incurred to ensure that the machine is working properly—it must be supervised. We construct a template for what automation should look like when the need for supervision is taken into account. The template can be applied to understand the arrangements for supervising automation in systems as they are and to explore the options for systems that are being designed. We consider examples from electronic warfare self-protection and the command and control of sensor-weapon systems in the land domain.

scholarly journals Economic Analysis of Model-Based Systems Engineering

Systems ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 12 ◽  
Author(s):  
Azad Madni ◽  
Shatad Purohit
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Cost Benefit Analysis ◽  
System Development ◽  
Cost Benefit ◽  
Cost Effective ◽  
Model Based ◽  
Regulatory Constraints ◽  
Long Run ◽  
Model Based Systems Engineering

In the face of ever-increasing complexity of systems and system development programs, several aerospace, automotive, and defense organizations have already begun or are contemplating the transition to model-based systems engineering (MBSE). The key challenges that organizations face in making this decision are determining whether it is technically feasible and financially beneficial in the long-run to transition to MBSE, and whether such transition is achievable given budgetary constraints. Among other cost drivers of this transition, are a new digital infrastructure, personnel training in MBSE, and cost-effective migration of legacy models and data into the new infrastructure. The ability to quantify gains from MBSE investment is critical to making the decision to commit to MBSE implementation. This paper proposes a methodological framework for analyzing investments and potential gains associated with MBSE implementation on large-scale system programs. To this end, the MBSE implementation problem is characterized in terms of: system complexity, environment complexity and regulatory constraints, and system lifespan. These criteria are applied to systems in twelve major industry sectors to determine MBSE investment and expected gains. Results from this cost-benefit analysis are used to justify investment in MBSE implementation where warranted. This approach is generic and can be applied to different sectors for economic evaluation of costs and benefits and justification of transition to MBSE if warranted.

Model-Based Estimation and Control System Development in a Urea-SCR Aftertreatment System

2008 ◽  
Vol 1 (1) ◽  
pp. 646-661 ◽  
Author(s):  
Maruthi Devarakonda ◽  
Gordon Parker ◽  
John H. Johnson ◽  
Vadim Strots ◽  
Shyam Santhanam
Keyword(s):  
Control System ◽  
System Development ◽  
Aftertreatment System ◽  
Model Based ◽  
Estimation And Control ◽  
And Control

A Taxonomy for Model-Based Systems Engineering

2021 ◽  
Author(s):  
João P. Monteiro ◽  
Paulo J. S. Gil ◽  
Rui M. Rocha
Keyword(s):  
Systems Engineering ◽  
System Development ◽  
Emergent Properties ◽  
System Specification ◽  
Academic Context ◽  
Model Based ◽  
Development Processes ◽  
Expected Benefits ◽  
Model Based Systems Engineering ◽  
Execution Models

Abstract In this paper, we define Model Based Systems Engineering (MBSE) as a set of different approaches which vary in scope and in purpose, as opposed to defining it as a monolithic concept. To do so, we inductively extract common themes from papers proposing new MBSE methods based on the type of Systems Engineering (SE) artifacts produced and the expected benefits of MBSE implementation. These themes are then validated against the experiences depicted in a second set of papers evaluating the deployment of MBSE methods in practice. We propose a taxonomy for MBSE which identifies three main categories: system specification repositories, system execution models, and design automation models. The proposed categories map well onto common discussions of the nature of the SE activity, in that the first is employed in the management of system development processes and the second in the understanding of system performance and emergent properties. The third category is almost exclusively discussed in an academic context and is therefore more difficult to relate to SE practice, but its features are clearly distinct from the other two. The proposed taxonomy clarifies what MBSE is and what it can be, therefore helping focus research on the issues that still prevent MBSE practice from living up to expectations.

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