scholarly journals Model Based Control System Design Using SysML, Simulink, and Computer Algebra System

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Takashi Sakairi ◽  
Eldad Palachi ◽  
Chaim Cohen ◽  
Yoichi Hatsutori ◽  
Junya Shimizu ◽  
...  

The Systems Modeling Language (SysML) is a standard, general-purpose, modeling language for model-based systems engineering (MBSE). SysML supports the specification, analysis, and design of a broad range of complex systems such as control systems. The authors demonstrate how they can integrate a SysML modeling tool (IBM Rational Rhapsody) with a proprietary simulation tool (MathWorks Simulink) and a Computer Algebra System (CAS) to validate system specification. The integration with Simulink enables users to perform systems engineering process in a SysML model, while designing continuous control algorithms and plant behavior in Simulink, and to validate the behavior by simulating the overall composition in Simulink. The integration with a CAS enables the evaluation of mathematical constraints defined in SysML parametric diagrams. The authors also show the overall approach using a Dual Clutch Transmission (DCT) and a Cruise Control System as examples.

2021 ◽  
Vol 8 (1) ◽  
pp. 39
Author(s):  
Andi Farmadi ◽  
Muliadi Muliadi

<p><em>Dissolved oxygen levels in water will affect water quality directly and indirectly for fish life as well as conditions in the water environment, therefore, it is very important to control water quality for adequate dissolved oxygen levels, because this plays an important role in the health condition of the environmental ecosystem for fish nurseries. Researchers usually measure and monitor water quality using measuring instruments that are widely sold in the market, for conditions of decreasing dissolved oxygen levels in fish nurseries tank can usually be controlled by adding an air bubble machine to the water using an aerator machine. Giving air bubbles to water is an effort to control the conditions for the adequacy of dissolved oxygen in the water, and the best system is to carry out a continuous control system regarding water quality, sometimes the oxygen condition in the water is sufficient for the standard of dissolved oxygen in water. However, the blower blower is still running, this is less effective because it requires unnecessary electrical energy or wastes energy. Analysis of the aerator engine control system is needed to make a design as to what state the aerator engine should be turned on. Analysis of the aerator engine control system can be done by measuring the level of oxygen and water temperature in the fish nursery tank, then designing a fuzzy model with the Sugeno inference system for how long the engine must be turned on. The analysis and design of this aerator system is a proposed solution to these problems with a system of measurement and monitoring carried out intelligently by a machine, so that it is able to measure how late this aerator machine must be turned on. and the developed design is capable of being a smart machine using a fuzzy system</em></p><p><strong><em>Keywords</em></strong><em>: Fuzzy inference, aerator engine, smart system, water quality.</em></p><p><em>Kadar oksigen terlarut dalam air akan mempengaruhi kualitas air secara langsung dan tidak langsung bagi kehidupan ikan juga keadaan di lingkungan air tersebut, oleh karena itu peningkatan kualitas air untuk keadaan kecukupan kadar oksigen yang terlarut sangat penting untuk dikontrol, karena hal ini berperan penting pada kondisi kesehatan ekosistem lingkungan pembibitan ikan. </em><em>Para peneliti biasanya melakukan pengukuran dan pemantauan kualitas air dengan menggunakan alat ukur yang banyak di jual dipasaran, untuk kondisi menurunnya kadar oksigen yang terlarut pada kolam pembibitan ikan biasanya dapat di kontrol dengan menambahkan mesin gelembung udara pada air menggunakan mesin aerator. Pemberian gelembung udara pada air merupakan salah satu upaya untuk mengontrol kondisi kecukupan kadar oksigen yang terlarut di dalam air, dan sistem yang terbaik yaitu melakukan sistem kontrol secara terus menerus mengenai kualitas air, terkadang kondisi oksigen di dalam air telah mencukupi standar kecukupan oksigen terlarut pada air, namun mesin penyembur gelembung udara masih dinyalakan, hal ini menjadi kurang efektif sebab akan membutuhkan energi listrik yang tidak semestinya atau terjadinya pemborosan energi. Analisis sistem pengontrolan mesin aerator dibutuhkan untuk melakukan desain seperti apa sebaiknya keadaan mesin aerator dihidupkan. Analisis sistem pengontrolan mesin aerator ini dapat dilakan dengan mengukur tingkat kadar oksigen dan suhu air pada kolam pembibitan ikan, kemudian melakukan perancangan model fuzzy dengan sistem inferensi sugeno seberapa lama mesin harus dihidupkan. Analisis dan desain sistem aerator ini merupakan usulan solusi permasalahan tersebut dengan sistem pengukuran dan pemantauan dilakukan secara cerdas oleh mesin, sehingga mampu mengukur seberapa lalma mesin aerator ini harus dihidupkan desain alat ini juga diharapkan mampu memberikan solusi peningkatan kualitas air pada pembibitan ikan dan diharapan pula analisis dan desain yang dikembangkan ini mampu menjadi mesin cerdas dengan menggukan sistem fuzzy</em></p><p><strong><em>Kata kunci</em></strong><em> : Fuzzy inferensi, mesin aerator, Sistem cerdas, kualitas air.</em></p>


2021 ◽  
Author(s):  
Haluk Altay ◽  
M. Furkan Solmazgül

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.


2020 ◽  
Vol 30 (1) ◽  
pp. 323-338
Author(s):  
Jinzhi Lu ◽  
Guoxin Wang ◽  
Junda Ma ◽  
Dimitris Kiritsis ◽  
Hang Zhang ◽  
...  

2021 ◽  
Author(s):  
João P. Monteiro ◽  
Paulo J. S. Gil ◽  
Rui M. Rocha

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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