Conference paper
A High-Performance Monte Carlo Simulation Toolbox for Uncertainty Quantification of Closed-loop Systems
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Center for Energy Resources Engineering, Centers, Technical University of Denmark3
Technical University of Denmark4
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2021 |
| Pages: | 6755-6761 |
| Proceedings: | 60th IEEE Conference on Decision and Control |
| ISBN: | 1665436603 , 9781665436601 , 1665436581 , 166543659X , 9781665436588 and 9781665436595 |
| ISSN: | 25762370 and 07431546 |
| Types: | Conference paper |
| DOI: | 10.1109/CDC45484.2021.9682781 |
| ORCIDs: | Wahlgreen, Morten Ryberg , Reenberg, Asbjørn Thode , Ritschel, Tobias K. S. , Dammann, Bernd and Jørgensen, John Bagterp |
