<abstract xmlns="http://www.w3.org/1999/xhtml"><p> This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop (HIL) that is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, linear-quadratic-Gaussian (LQG)-type control, and the nonlinear model of a maglev suspension. A robustness analysis of the closed-loop is followed (prior to implementation) supporting the appropriateness of the solution under parametric variation. The analysis also shows that quantization is robust under different controller gains. While the LQG controller is implemented on an FPGA, the physical process is realized in a high-level system modeling environment. FPGA technology enables rapid evaluation of the algorithms and test designs under realistic scenarios avoiding heavy time penalty associated with hardware description language (HDL) simulators. The HIL technique facilitates significant speed-up in the required execution time when compared to its software-based counterpart model.</p></abstract>

This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop (HIL) that is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, linear-quadratic-Gaussian (LQG)-type control, and the nonlinear model of a maglev suspension. A robustness analysis of the closed-loop is followed (prior to implementation) supporting the appropriateness of the solution under parametric variation. The analysis also shows that quantization is robust under different controller gains. While the LQG controller is implemented on an FPGA, the physical process is realized in a high-level system modeling environment. FPGA technology enables rapid evaluation of the algorithms and test designs under realistic scenarios avoiding heavy time penalty associated with hardware description language (HDL) simulators. The HIL technique facilitates significant speed-up in the required execution time when compared to its software-based counterpart model.

FPGA–Based Efficient Hardware/Software Co–Design for Industrial Systems with Consideration of Output Selection

Department of Electrical, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, CY

School of Engineering, College of Science, University of Lincoln, UK

School of Electrical and Computer Engineering, National Technical University of Athens, GR

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop...

FPGA–Based Efficient Hardware/Software Co–Design for Industrial Systems with Consideration of Output Selection

Published Online: Jun 28, 2016

Page range: 150 - 159

Received: Jan 17, 2016

DOI: https://doi.org/10.1515/jee-2016-0022

Keywords
sensor optimization, FPGA, maglev, embedded control, electromagnetic suspension, linear quadratic Gaussian, hardware-in-the-loop, FPGA-in-the-loop

© Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

FPGA–Based Efficient Hardware/Software Co–Design for Industrial Systems with Consideration of Output Selection

Published Online: Jun 28, 2016

Page range: 150 - 159

Received: Jan 17, 2016

DOI: https://doi.org/10.1515/jee-2016-0022

Keywordssensor optimization, FPGA, maglev, embedded control, electromagnetic suspension, linear quadratic Gaussian, hardware-in-the-loop, FPGA-in-the-loop

© Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
sensor optimization, FPGA, maglev, embedded control, electromagnetic suspension, linear quadratic Gaussian, hardware-in-the-loop, FPGA-in-the-loop