A High-Performance Monte Carlo Simulation Toolbox for Uncertainty Quantification of Closed-loop Systems

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Abstract

We apply Monte Carlo simulation for performance quantification and tuning of controllers in nonlinear closed-loop systems. Computational feasibility of large-scale Monte Carlo simulation is achieved by implementation of a parallelized high-performance Monte Carlo simulation toolbox for closed-loop systems in C for shared memory architectures. The toolbox shows almost linear scale-up on 16 CPU cores on a single NUMA node, and a scale-up of 27.3 on two NUMA nodes with a total of 32 CPU cores. We demonstrate performance quantification and tuning of a PID controller for a bioreactor in fed-batch operation. We perform 30,000 closed-loop simulations of the fed-batch reactor within 1 second. This is approximately a 2300 times computational performance increase compared to a serial reference implementation in Matlab. Additionally, we apply Monte Carlo simulation to perform automatic tuning of the PID controller based on maximizing average produced biomass within 8 seconds.
Original languageEnglish
Title of host publicationProceedings of 60th IEEE Conference on Decision and Control
PublisherIEEE
Publication date17 Dec 2021
Pages6755-6761
Article number9682781
ISBN (Print)978-1-6654-3660-1
DOIs
Publication statusPublished - 17 Dec 2021
Event60th IEEE Conference on Decision and Control - Virtual Conference, Austin, United States
Duration: 14 Dec 202117 Dec 2021
Conference number: 60
https://ieeexplore.ieee.org/xpl/conhome/9682670/proceeding
https://2021.ieeecdc.org/

Conference

Conference60th IEEE Conference on Decision and Control
Number60
LocationVirtual Conference
Country/TerritoryUnited States
CityAustin
Period14/12/202117/12/2021
Internet address

Keywords

  • Drugs
  • Monte Carlo methods
  • Uncertainty
  • Memory architecture
  • Probability density function
  • Closed loop systems
  • Nonlinear systems

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