As embedded control devices become more common in today's electro-mechanical systems, HIL Simulation is growing in its importance to the success of these systems. HIL testing provides a simulated environment for t...
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ISBN:
(纸本)9781424479597
As embedded control devices become more common in today's electro-mechanical systems, HIL Simulation is growing in its importance to the success of these systems. HIL testing provides a simulated environment for the unit under test, simulating the parts of the system that are not physically present. As these systems grow in complexity, traditional HIL simulation techniques are falling short. Fortunately, technologies such as Field Programmable Gate Arrays (FPGAs) are being applied to produce the next generation of HIL simulators. FPGAs enable test system developers to create custom hardware that can be easily reconfigured without physically modifying the device. In addition to being reconfigurable, for certain applications, FPGAs can offer superior performance compared to microprocessors. More specifically for HIL test systems, FPGA-based I/O devices provide superior determinism, on the order of nanoseconds, enabling realistic simulation of plant components not typically realizable with microprocessor-only based systems. They are also used to off-load some of the processing that would otherwise be required of the test system microprocessor increasing the total system bandwidth. Because of the ease with which their personalities can be reconfigured, FPGAs are also used in HIL test systems to create custom IO interfaces as well as IO interfaces that can adapt to multiple UUT types or changes to UUT interfaces that evolve during product development. In this paper, we will discuss examples of how FPGAs are being used for sensor simulation to create better, more adaptable HIL test systems.
Fuzzy models can be conveniently regarded as linguistic modelling structures with well-defined functional blocks of input and outputinterfaces along with a processing module. This paper examines the functions of thes...
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Fuzzy models can be conveniently regarded as linguistic modelling structures with well-defined functional blocks of input and outputinterfaces along with a processing module. This paper examines the functions of these modules and specifies the relevant optimization tasks which accrue to them. Considering several levels of memorization completed within fuzzy models (resulting in establishing short-, medium- and long-term memories), the corresponding learning policies are developed. Some classes of optimization mechanisms are also discussed, with particular emphasis focused on their ability to handle multimodal problems. These tools comprise both gradient-based techniques and selected methods of evolutionary optimization, in particular genetic algorithms. Illustrative numerical studies are also included.
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