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Model-based design

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247:) model, or by simulating a non-linear model of the plant with the controller. Simulation allows specification, requirements, and modeling errors to be found immediately, rather than later in the design effort. Real-time simulation can be done by automatically generating code for the controller developed in step 2. This code can be deployed to a special real-time prototyping computer that can run the code and control the operation of the plant. If a plant prototype is not available, or testing on the prototype is dangerous or expensive, code can be automatically generated from the plant model. This code can be deployed to the special real-time computer that can be connected to the target processor with running controller code. Thus a controller can be tested in real-time against a real-time plant model. 226:. With system identification, the plant model is identified by acquiring and processing raw data from a real-world system and choosing a mathematical algorithm with which to identify a mathematical model. Various kinds of analysis and simulations can be performed using the identified model before it is used to design a model-based controller. First-principles based modeling is based on creating a block diagram model that implements known differential-algebraic equations governing plant dynamics. A type of first-principles based modeling is physical modeling, where a model consists in connected blocks that represent the physical elements of the actual plant. 282:: Model-based design can encounter significant challenges due to the lack of high-quality tools for managing version control, particularly for handling diff and merge operations. This can lead to difficulties in managing concurrent changes and maintaining robust revision control practices. Although newer tools, such as 3-way merge, have been introduced to address these issues, effectively integrating these solutions into existing workflows remains a complex task. 178:
building blocks. These built models used with simulation tools can lead to rapid prototyping, software testing, and verification. Not only is the testing and verification process enhanced, but also, in some cases, hardware-in-the-loop simulation can be used with the new design paradigm to perform testing of dynamic effects on the system more quickly and much more efficiently than with traditional design methodology.
206:(PLC) mimicked the operations of already available discrete control technologies that used the out-dated relay ladders. The advent of PC technology brought a drastic shift in the process and discrete control market. An off-the-shelf desktop loaded with adequate hardware and software can run an entire process unit, and execute complex and established PID algorithms or work as a Distributed Control System (DCS). 29: 315:
model fidelity by simply substituting one block element with another. Graphical models also help engineers to conceptualize the entire system and simplify the process of transporting the model from one stage to another in the design process. Boeing's simulator EASY5 was among the first modeling tools to be provided with a graphical user interface, together with
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Graphical modeling tools aim to improve these aspects of design. These tools provide a very generic and unified graphical modeling environment, and they reduce the complexity of model designs by breaking them into hierarchies of individual design blocks. Designers can thus achieve multiple levels of
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Because of the limitations of graphical tools, design engineers previously relied heavily on text-based programming and mathematical models. However, developing these models was time-consuming, and highly prone to error. In addition, debugging text-based programs is a tedious process, requiring much
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Design and reuse patterns can lead to implementations of models that are not well suited to that task. Such as implementing a controller for a conveyor belt production facility that uses a thermal sensor, speed sensor, and current sensor. That model is generally not well suited for re-implementation
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Deployment. Ideally this is done via code generation from the controller developed in step 2. It is unlikely that the controller will work on the actual system as well as it did in simulation, so an iterative debugging process is carried out by analyzing results on the actual target and updating the
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While Model-based design has the ability to simulate test scenarios and interpret simulations well, in real world production environments, it is often not suitable. Over reliance on a given toolchain can lead to significant rework and possibly compromise entire engineering approaches. While it's
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Control systems gained momentum, primarily in the automotive and aerospace sectors. In the 1950s and 1960s, the push to space generated interest in embedded control systems. Engineers constructed control systems such as engine control units and flight simulators, that could be part of the end
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As early as the 1920s two aspects of engineering, control theory and control systems, converged to make large-scale integrated systems possible. In those early days controls systems were commonly used in the industrial environment. Large process facilities started using process controllers for
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The model-based design is significantly different from traditional design methodology. Rather than using complex structures and extensive software code, designers can use Model-based design to define plant models with advanced functional characteristics using continuous-time and discrete-time
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One major disadvantage is that the approach taken is a blanket or coverall approach to standard embedded and systems development. Often the time it takes to port between processors and ecosystems can outweigh the temporal value it offers in the simpler lab based
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regulating continuous variables such as temperature, pressure, and flow rate. Electrical relays built into ladder-like networks were one of the first discrete control devices to automate an entire manufacturing process.
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analysis and synthesis. The mathematical model conceived in step 1 is used to identify dynamic characteristics of the plant model. A controller can then be synthesized based on these characteristics.
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Much of the compilation tool chain is closed source, and prone to fence post errors, and other such common compilation errors that are easily corrected in traditional systems engineering.
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trial and error before a final fault-free model could be created, especially since mathematical models undergo unseen changes during the translation through the various design stages.
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Model-based design provides a common design environment, which facilitates general communication, data analysis, and system verification between various (development) groups.
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Model-based design provides an efficient approach for establishing a common framework for communication throughout the design process while supporting the development cycle (
577: 327:, which allowed models to be composed of physical components like masses, springs, resistors, etc. These were later followed by many other modern tools such as 474: 549: 507: 523: 303:
Engineers can locate and correct errors early in system design, when the time and financial impact of system modification are minimized.
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The disadvantages of model-based design are fairly well understood this late in development lifecycle of the product and development.
364: 243:. The time response of the dynamic system to complex, time-varying inputs is investigated. This is done by simulating a simple LTI ( 434: 112: 93: 65: 50: 251:
controller model. Model-based design tools allow all these iterative steps to be performed in a unified visual environment.
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General Motors Developed Two-Mode Hybrid Powertrain With MathWorks Model-Based Design; Cut 24 Months Off Expected Dev Time
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in a motor controller etc. Though its very easy to port such a model over, and introduce all the software faults therein.
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A Software Safety Certification Plug-in for Automated Code Generators: Feasibility Study and Preliminary Design
354: 349: 61: 319:, a multi-domain, multi-level platform based on the Bond Graph theory. This was soon followed by tool like 244: 144:, and automotive applications. Model-based design is a methodology applied in designing embedded software. 161: 17: 288:
suitable for bench work, the choice to use this for a production system should be made very carefully.
223: 132:) is a mathematical and visual method of addressing problems associated with designing complex control, 413: 359: 240: 369: 527: 449: 306:
Design reuse, for upgrades and for derivative systems with expanded capabilities, is facilitated.
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Some of the advantages model-based design offers in comparison to the traditional approach are:
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product. By the end of the twentieth century, embedded control systems were ubiquitous, as even
86: 503: 430: 133: 498:. Computational Analysis, Synthesis, and Design of Dynamic Systems. Vol. 1. Boca Raton: 557: 491: 422: 394: 196: 156:). In model-based design of control systems, development is manifested in these four steps: 581: 478: 219: 199:
contained complex and advanced control algorithms, making them much more "intelligent".
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Model Based Design Accelerates the Development of Mechanical Locomotive Controls
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Model-based design for mechatronics systems, Machine Design, November 21, 2007
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In 1969, the first computer-based controllers were introduced. These early
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Automakers Opting for Model-Based Design, Design News, November 5, 2010
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Plant modeling. Plant modeling can be data-driven or based on
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Ahmadian, M.; Nazari, Z. J.; Nakhaee, N.; Kostic, Z. (2005).
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Model-based design of software is not to be confused with
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integrating all these phases by deploying the controller.
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analyzing and synthesizing a controller for the plant,
222:. Data-driven plant modeling uses techniques such as 393:. SAE 2010 Commercial Vehicle Engineering Congress. 214:The main steps in model-based design approach are: 53:. Unsourced material may be challenged and removed. 548:Sauceda, Jeremias; Kothari, Suraj (2010-04-12). 136:and communication systems. It is used in many 8: 524:"Model-based design reshaping Disney parks" 113:Learn how and when to remove this message 554:SAE 2010 World Congress & Exhibition 496:Model-Based Design for Embedded Systems 381: 7: 170:simulating the plant and controller, 51:adding citations to reliable sources 401:. SAE Technical Paper 2010-01-1999. 193:major household consumer appliances 365:Specification (technical standard) 14: 27: 389:Reedy, J.; Lunzman, S. (2010). 38:needs additional citations for 204:programmable logic controllers 1: 195:such as washing machines and 616: 580:November 25, 2010, at the 477:November 25, 2010, at the 415:Model based design and SDR 15: 556:. SAE International: 10. 355:Model-driven engineering 350:Functional specification 140:, industrial equipment, 280:Version control issues 18:model-based definition 490:Nicolescu, Gabriela; 245:Linear Time-Invariant 224:System identification 562:10.4271/2010-01-0940 492:Mosterman, Pieter J. 399:10.4271/2010-01-1999 360:Scientific modelling 241:real-time simulation 62:"Model-based design" 47:improve this article 20:of physical objects. 600:Control engineering 427:10.1049/ic:20050389 370:Systems engineering 126:Model-based design 509:978-1-4200-6784-2 134:signal processing 123: 122: 115: 97: 607: 584: 572: 566: 565: 545: 539: 538: 536: 535: 526:. Archived from 520: 514: 513: 487: 481: 469: 463: 458: 452: 447: 441: 440: 420: 409: 403: 402: 386: 265:implementations. 220:first principles 197:air conditioners 118: 111: 107: 104: 98: 96: 55: 31: 23: 615: 614: 610: 609: 608: 606: 605: 604: 590: 589: 588: 587: 582:Wayback Machine 573: 569: 547: 546: 542: 533: 531: 522: 521: 517: 510: 494:, eds. (2010). 489: 488: 484: 479:Wayback Machine 470: 466: 459: 455: 448: 444: 437: 418: 411: 410: 406: 388: 387: 383: 378: 341: 294: 286: 258: 212: 184: 150: 119: 108: 102: 99: 56: 54: 44: 32: 21: 12: 11: 5: 613: 611: 603: 602: 592: 591: 586: 585: 567: 540: 515: 508: 482: 464: 453: 442: 435: 404: 380: 379: 377: 374: 373: 372: 367: 362: 357: 352: 347: 345:Control theory 340: 337: 308: 307: 304: 301: 293: 290: 284: 283: 277: 272: 271: 267: 266: 257: 254: 253: 252: 248: 233: 227: 211: 208: 183: 180: 175: 174: 171: 168: 165: 149: 146: 138:motion control 121: 120: 35: 33: 26: 13: 10: 9: 6: 4: 3: 2: 612: 601: 598: 597: 595: 583: 579: 576: 571: 568: 563: 559: 555: 551: 544: 541: 530:on 2016-08-28 529: 525: 519: 516: 511: 505: 501: 497: 493: 486: 483: 480: 476: 473: 468: 465: 462: 457: 454: 451: 446: 443: 438: 436:0-86341-560-1 432: 428: 424: 417: 416: 408: 405: 400: 396: 392: 385: 382: 375: 371: 368: 366: 363: 361: 358: 356: 353: 351: 348: 346: 343: 342: 338: 336: 334: 330: 326: 322: 318: 312: 305: 302: 299: 298: 297: 291: 289: 281: 278: 274: 273: 269: 268: 263: 262: 261: 256:Disadvantages 255: 249: 246: 242: 238: 234: 231: 228: 225: 221: 217: 216: 215: 209: 207: 205: 200: 198: 194: 188: 181: 179: 172: 169: 166: 163: 159: 158: 157: 155: 147: 145: 143: 139: 135: 131: 127: 117: 114: 106: 95: 92: 88: 85: 81: 78: 74: 71: 67: 64: –  63: 59: 58:Find sources: 52: 48: 42: 41: 36:This article 34: 30: 25: 24: 19: 570: 553: 543: 532:. Retrieved 528:the original 518: 495: 485: 467: 456: 445: 414: 407: 390: 384: 313: 309: 295: 285: 279: 259: 213: 201: 189: 185: 176: 151: 129: 125: 124: 109: 100: 90: 83: 76: 69: 57: 45:Please help 40:verification 37: 160:modeling a 103:August 2018 534:2016-02-18 376:References 292:Advantages 237:simulation 230:Controller 73:newspapers 500:CRC Press 142:aerospace 594:Category 578:Archived 475:Archived 339:See also 329:Simulink 235:Offline 148:Overview 333:LabVIEW 182:History 154:V-model 87:scholar 506:  433:  325:Dymola 321:20-sim 317:AMESim 89:  82:  75:  68:  60:  419:(PDF) 210:Steps 162:plant 94:JSTOR 80:books 504:ISBN 431:ISBN 331:and 323:and 239:and 66:news 558:doi 423:doi 395:doi 130:MBD 49:by 596:: 552:. 502:. 429:. 335:. 564:. 560:: 537:. 512:. 439:. 425:: 397:: 164:, 128:( 116:) 110:( 105:) 101:( 91:· 84:· 77:· 70:· 43:.

Index

model-based definition

verification
improve this article
adding citations to reliable sources
"Model-based design"
news
newspapers
books
scholar
JSTOR
Learn how and when to remove this message
signal processing
motion control
aerospace
V-model
plant
major household consumer appliances
air conditioners
programmable logic controllers
first principles
System identification
Controller
simulation
real-time simulation
Linear Time-Invariant
AMESim
20-sim
Dymola
Simulink

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