arXiv subjects

Mehmet Mercangoz

Publications and source records attributed to Mehmet Mercangoz.

4 recordsLinked to original sources

arXiv2026

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction

A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with minimal or no manual redesign. In this setting, policy generation by AI agents can be a credible path when paired with a plant-aware validator (e.g., a digital twin) that can check generated candidate actions before execution. However, practical deployment is constrained by inference latency and compute footprint: large cloud-based models are often too slow, opaque, or data-sensitive for edge closed-loop use. This work investigates whether a compact Small Language Model (SLM) can be retrained for control reasoning and embedded in a validator-guided correction loop. We use a Qwen2.5-1.5B model aligned via Group Relative Policy Optimization (GRPO), combined with (i) an action agent, (ii) a symbolic/digital-twin-style validation layer, and (iii) a reprompting agent that iteratively steers outputs toward valid actions. In randomized thermal-control simulations (30 experiments with 500 steps each), the framework achieves 91.5% average action-alignment accuracy (86.3%--100% across cases) at 3.84\,s mean inference latency. Under symbolic re-mapping, it maintains a 95% in-range rate, indicating robust physical regulation despite reduced token-level agreement. These results support SLM+validator architectures as a practical path toward reconfigurable autonomous control at the edge.

cs.AI↗

arXiv2025

Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation

The increasing complexity of modern chemical processes, coupled with workforce shortages and intricate fault scenarios, demands novel automation paradigms that blend symbolic reasoning with adaptive control. In this work, we introduce a unified agentic framework that leverages large language models (LLMs) for both discrete fault-recovery planning and continuous process control within a single architecture. We adopt Finite State Machines (FSMs) as interpretable operating envelopes: an LLM-driven planning agent proposes recovery sequences through the FSM, a Simulation Agent executes and checks each transition, and a Validator-Reprompting loop iteratively refines invalid plans. In Case Study 1, across 180 randomly generated FSMs of varying sizes (4-25 states, 4-300 transitions), GPT-4o and GPT-4o-mini achieve 100% valid-path success within five reprompts-outperforming open-source LLMs in both accuracy and latency. In Case Study 2, the same framework modulates dual-heater inputs on a laboratory TCLab platform (and its digital twin) to maintain a target average temperature under persistent asymmetric disturbances. Compared to classical PID control, our LLM-based controller attains similar performance, while ablation of the prompting loop reveals its critical role in handling nonlinear dynamics. We analyze key failure modes-such as instruction following lapses and coarse ODE approximations. Our results demonstrate that, with structured feedback and modular agents, LLMs can unify high-level symbolic planningand low-level continuous control, paving the way towards resilient, language-driven automation in chemical engineering.

cs.AI↗

arXiv2024

ARRTOC: Adversarially Robust Real-Time Optimization 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.

eess.SY↗

arXiv2022

Learning Linear Representations of Nonlinear Dynamics Using Deep Learning

The vast majority of systems of practical interest are characterised by nonlinear dynamics. This renders the control and optimization of such systems a complex task due to their nonlinear behaviour. Additionally, standard methods such as linearizing around a fixed point may not be an effective strategy for many systems, thus requiring an alternative approach. For this reason, we propose a new deep learning framework to discover a transformation of a nonlinear dynamical system to an equivalent higher dimensional linear representation. We demonstrate that the resulting learned linear representation accurately captures the dynamics of the original system for a wider range of conditions than standard linearization. As a result of this, we show that the learned linear model can subsequently be used for the successful control of the original system. We demonstrate this by applying the proposed framework to two examples; one from the literature and a more complex example in the form of a Continuous Stirred Tank Reactor (CSTR).

eess.SY↗