3,218
edits
Difference between revisions of "MBD Software"
Jump to navigation
Jump to search
no edit summary
(Created page with "Category:Software{{Knoppen}} <noinclude><!------------------------------------------------ * READ THIS FIRST * Only edit this page if you can improve the content. * Improper...") |
|||
| Line 1: | Line 1: | ||
[[Category:Software]]{{Knoppen}} | [[Category:Software]]{{Knoppen}} | ||
Model-Based Design (MBD) is a mathematical and visual method of addressing problems associated with designing complex control,[1][2] signal processing[3] and communication systems. It is used in many motion control, industrial equipment, aerospace, and automotive applications.[4][5] Model-based design is a methodology applied in designing embedded software.[6][7] | |||
MBD provides an efficient approach for establishing a common framework for communication throughout the design process while supporting the development cycle ("V" diagram). In model-based design of control systems, development is manifested in these four steps: 1) modeling a plant, 2) analyzing and synthesizing a controller for the plant, 3) simulating the plant and controller, and 4) integrating all these phases by deploying the controller. The model-based design paradigm is significantly different from traditional design methodology. Rather than using complex structures and extensive software code, designers can use MBD to define models with advanced functional characteristics using continuous-time and discrete-time 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. | |||
The main steps in MBD approach are: | |||
Plant modeling. Plant modeling can be data-driven or first principles based. Data-driven plant modeling uses techniques such as System identification. 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 is created by connecting blocks that represent the physical elements that the actual plant consists of.Controller analysis and synthesis. The mathematical model conceived in step 1 is used to identify dynamic characteristics of the plant model. A controller can be then be synthesized based on these characteristics.Offline simulation and real-time simulation. The time response of the dynamic system to complex, time-varying inputs is investigated. This is done by simulating a simple LTI or 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 protoyping 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.Deployment. Ideally this is done via automatic 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 controller model. Model based design tools allow all these iterative steps to be performed in a unified visual environment. | |||
Some of the notable advantages MBD offers in comparison to the traditional approach are:[8] | |||
MBD provides a common design environment, which facilitates general communication, data analysis, and system verification between development groups.Engineers can locate and correct errors early in system design, when the time and financial impact of system modification are minimized.Design reuse, for upgrades and for derivative systems with expanded capabilities, is facilitated | |||