Model-Based Systems Engineering and Control System Development via Virtual Hardware-in-the-Loop Simulation 2010-01-2325
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. A key element of this technique is the software-in-the-loop (SIL) capability, which permits compiled production controller code to be incorporated into the simulation environment, thus allowing the inclusion of algorithm functionality for which no simulation models exist. With this approach, the control system can be developed early in the vehicle or powertrain design cycle, incorporating plant models, algorithm models, existing controller code, and architectural constructs that greatly expedite the creation of a system simulation that can be used for algorithm development, testing, and validation. An application of this methodology at General Motors Powertrain is described in detail.
Citation: Michaels, L., Pagerit, S., Rousseau, A., Sharer, P. et al., "Model-Based Systems Engineering and Control System Development via Virtual Hardware-in-the-Loop Simulation," SAE Technical Paper 2010-01-2325, 2010, https://doi.org/10.4271/2010-01-2325. Download Citation
Lawrence Michaels, Sylvain Pagerit, Aymeric Rousseau, Phillip Sharer, Shane Halbach, Ram Vijayagopal, Michael Kropinski, Gregory Matthews, Minghui Kao, Onassis Matthews, Michael Steele, Anthony Will
Argonne National Laboratory, General Motors LLC