# ARRTOC: Adversarially Robust Real-Time Optimization and Control

arXiv: https://arxiv.org/abs/2309.04386 · HTML: https://mlsanity.com/paper/2309.04386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development](https://mlsanity.com/paper/2606.19230/) (2026)

## Abstract (arXiv)

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.