Abstract

As the performance of digital devices is improving, Hardware-In-the-Loop (HIL) techniques are being increasingly used. HIL systems are frequently implemented using FPGAs (Field Programmable Gate Array) as they allow faster calculations and therefore smaller simulation steps. As the simulation step is reduced, the incremental values for the state variables are reduced proportionally, increasing the difference between the current value of the state variable and its increments. This difference can lead to numerical resolution issues when both magnitudes cannot be stored simultaneously in the state variable. FPGA-based HIL systems generally use 32-bit floating-point due to hardware and timing restrictions but they may suffer from these resolution problems. This paper explores the limits of 32-bit floating-point arithmetics in the context of hardware-in-the-loop systems, and how a larger format can be used to avoid resolution problems. The consequences in terms of hardware resources and running frequency are also explored. Although the conclusions reached in this work can be applied to any digital device, they can be directly used in the field of FPGAs, where the designer can easily use custom floating-point arithmetics.

Highlights

  • Digital control for power converters has been growing during the past two decades [1,2,3,4,5]

  • This paper explores the limits of floating-point arithmetics for HIL systems, and how to predict the floating-point format needed for accurate simulations

  • Thanks to the improvement in the performance of digital devices, HIL systems are starting to be used in applications that require small simulation steps

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Summary

Introduction

Digital control for power converters has been growing during the past two decades [1,2,3,4,5]. In floating-point arithmetics, the designer does not take this definition into consideration, as an IEEE-754 single-precision floating-point number can store values up to ±2127 , and the resolution is optimized in every calculation. This is accomplished by the floating-point libraries which automatically adapt the point location through the exponent field. Because of this remarkable advantage of floating-point, most HIL models use floating-point arithmetics [9,27,28], including commercial implementations [18,19,20].

Application Example
Numerical Resolution
Simulation Results
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