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Technical Paper
Lawrence Michaels, Sylvain Pagerit, Aymeric Rousseau, Phillip Sharer, Shane Halbach, Ram Vijayagopal, Michael Kropinski, Gregory Matthews, Minghui Kao, Onassis Matthews, Michael Steele, Anthony Will
Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.
Technical Paper
Cheryl A. Williams, Michael A. Kropinski, Onassis Matthews, Michael A. Steele
Currently in the automotive industry, most software source code is manually generated (i.e., hand written). This manually generated code is written to satisfy requirements that are generally specified or captured in an algorithm document. However, this process can be very error prone since errors can be introduced during the manual translation of the algorithm document to code. A better method would be to automatically generate code directly from the algorithm document. Therefore, the automotive industry is striving to model new and existing algorithms in an executable-modeling paradigm where code can be automatically generated. The advent of executable models together with automatic code generation should allow the translation of model to code to be error free, and this error-free status can be confirmed through testing. A three-stage process is presented to functionally verify the model, functionally verify the automatically-generated code, and structurally verify the code.
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