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| 001 | 894319 | ||
| 005 | 20240712112901.0 | ||
| 024 | 7 | _ | |a 10.1016/j.ifacol.2020.12.1207 |2 doi |
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| 037 | _ | _ | |a FZJ-2021-03179 |
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| 100 | 1 | _ | |a Doncevic, Danimir |0 P:(DE-Juel1)180221 |b 0 |u fzj |
| 111 | 2 | _ | |a 1st Virtual IFAC World Congress |c online |d 2020-07-11 - 2020-07-17 |w Germany |
| 245 | _ | _ | |a Deterministic Global Nonlinear Model Predictive Control with Neural Networks Embedded |
| 260 | _ | _ | |a Laxenburg |c 2020 |b IFAC |
| 300 | _ | _ | |a 5273 - 5278 |
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| 520 | _ | _ | |a Nonlinear model predictive control requires the solution of nonlinear programs with potentially multiple local solutions. Here, deterministic global optimization can guarantee to find a global optimum. However, its application is currently severely limited by computational cost and requires further developments in problem formulation, optimization solvers, and computing architectures. In this work, we propose a reduced-space formulation for the global optimization of problems with recurrent neural networks (RNN) embedded, based on our recent work on feed-forward artificial neural networks embedded. The method reduces the dimensionality of the optimization problem significantly, lowering the computational cost. We implement the NMPC problem in our open-source solver MAiNGO and solve it using parallel computing on 40 cores. We demonstrate real-time capability for the illustrative van de Vusse CSTR case study. We further propose two alternatives to reduce computational time: i) reformulate the RNN model by exposing a selected state variable to the optimizer; ii) replace the RNN with a neural multi-model. In our numerical case studies each proposal results in a reduction of computational time by an order of magnitude. |
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| 700 | 1 | _ | |a Schweidtmann, Artur M. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Vaupel, Yannic |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Schäfer, Pascal |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Caspari, Adrian |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 5 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1016/j.ifacol.2020.12.1207 |g Vol. 53, no. 2, p. 5273 - 5278 |0 PERI:(DE-600)2839185-8 |n 2 |p 5273 - 5278 |t IFAC-PapersOnLine |v 53 |y 2020 |x 2405-8963 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/894319/files/1-s2.0-S2405896320316037-main.pdf |y OpenAccess |
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