Model-Based Design is no longer limited to R&D and pilot programs; it is frequently used for production programs at automotive companies around the world. The demands of production programs drive an even greater need for tools and practices that enable automation and rigor in the area of verification, validation, and test. Without these tools and practices, achieving the quality demanded by the automotive market is not possible. This paper presents best practices in verification, validation, and test that are applicable to any program, but are critical when applying Model-Based Design in production programs.
This paper studies a number of different techniques that can be used to reduce the amount of time needed to run block diagram simulations. The first is automatic code generation techniques used to create simulation executables from graphical block diagram models. A number of alternative techniques are studied, highlighting increases in simulation speed that can be achieved at the expense of interactivity with the graphical model. This paper will discuss at which stages of modeling and simulation code generation should be considered. The second technique that is studied is the use of computing clusters to distribute a number of simulation runs across a number of processors. With the advent of the multicore processor this technique has become accessible to many more engineers than in the past.
Model-Based Design has become a standard in the aerospace industry. A system model is at the center of the development process, supporting the design of complex algorithms combined with physical hardware. In addition to the advantages that come from modeling control algorithms, modeling plants can lead to more robust designs. Using commercial-off-the-shelf (COTS) software allows both the controller and hardware to be modeled and simulated in a single environment. Modeling plants in the same simulation environment as an embedded controller enables engineers to test a controller with multiple plant parameters, as well as simulating with nominal or ideal values. Modeling variable physical parameters provides a better representation of what will happen in real hardware. Monte Carlo analysis is a standard method of simulating variability that occurs in real physical parameters. In aerospace applications, Monte Carlo techniques can be used to ensure highquality, robust designs. Even using a shared COTS environment, fully testing or optimizing a design can take thousands of simulation iterations and days to complete. Depending upon the complexity of the system and fidelity of the model, each iteration could take hours to run. Simulation time can become a critical bottleneck in the development process. Being able to run multiple, independent scenarios in parallel can lessen this time significantly. In this paper, we will discuss techniques for modeling, optimizing, and testing plant models to build better system models in MATLAB® and Simulink® from The MathWorks. We will also present new techniques for speeding up Monte Carlo techniques by using high-performance computing clusters.
Model-Based Design has become a standard in the automotive industry. In addition to the well-documented advantages that come from modeling control algorithms, [1,2,3,4] modeling plants can lead to more robust designs. Plant modeling enables engineers to test a controller with multiple plant parameters, and to simulate nominal or ideal values. Modeling variable physical parameters provides a better representation of what can be expected in production. Monte Carlo analysis is a standard method of simulating variability that occurs in real physical parameters. Automotive companies use Monte Carlo testing to ensure high quality, robust designs. Due to time and resource constraints, engineers often examine only a limited number of key parameters rather than an entire set. This leaves the design vulnerable to problems caused by missing the full potential impact of parameters that were unvaried during testing. New high-performance computing tools and multiprocessor machines have eliminated the time and resource limitations in many cases by providing the processing power needed to vary large numbers of parameters in complex dynamic models. This paper presents new methods for distributing Monte Carlo analyses of system models across multiple machines. These methods reduce testing time and enable more complete analyses, ensuring better quality when designs go into production.
Segmented electronic paper displays are ultra-low power devices, particularly in applications where information is not updated continuously. This paper will present a new integrated module design using standard interfaces and off-theshelf processors. Also addressed are the reliability and lifetime performance of segmented electronic paper displays.
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