ARRTOC: Adversarially Robust Real-Time Optimization and Control
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910353725587456 |
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| author | Ahmed, Akhil del Rio-Chanona, Ehecatl Antonio Mercangoz, Mehmet |
| author_facet | Ahmed, Akhil del Rio-Chanona, Ehecatl Antonio Mercangoz, Mehmet |
| contents | Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the lower-level controllers. However, at the control layer, these set-points may be difficult to track due to challenges in implementation as a result of disturbances, measurement noise, and actuator performance limitations. To address this, in this paper, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC addresses this issue by finding set-points which are both optimal and inherently robust to implementation errors at the control layers. ARRTOC draws inspiration from adversarial machine learning, offering a novel constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. By integrating controller design with RTO, ARRTOC enhances overall system performance and robustness by ensuring the chosen set-points are tailored to the underlying controller designs. To validate our claims, we present three case studies: an illustrative example, a bioreactor case study, and a multi-loop evaporator process. The proposed approach is found to improve RTO objectives, such as profit, by as much as $50\%$ in some case studies compared to RTO formulations which ignore the performance of the control layers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_04386 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ARRTOC: Adversarially Robust Real-Time Optimization and Control Ahmed, Akhil del Rio-Chanona, Ehecatl Antonio Mercangoz, Mehmet Systems and Control Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the lower-level controllers. However, at the control layer, these set-points may be difficult to track due to challenges in implementation as a result of disturbances, measurement noise, and actuator performance limitations. To address this, in this paper, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC addresses this issue by finding set-points which are both optimal and inherently robust to implementation errors at the control layers. ARRTOC draws inspiration from adversarial machine learning, offering a novel constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. By integrating controller design with RTO, ARRTOC enhances overall system performance and robustness by ensuring the chosen set-points are tailored to the underlying controller designs. To validate our claims, we present three case studies: an illustrative example, a bioreactor case study, and a multi-loop evaporator process. The proposed approach is found to improve RTO objectives, such as profit, by as much as $50\%$ in some case studies compared to RTO formulations which ignore the performance of the control layers. |
| title | ARRTOC: Adversarially Robust Real-Time Optimization and Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2309.04386 |