From Detection to Action: Using LLM Agents for Fault-Tolerant Control
We propose an agentic Large Language Model (LLM) framework for active Fault-Tolerant Control (FTC) that transforms fault detection outputs into constraint-aware recovery actions grounded in plant-specific knowledge. The approach couples (i) a multi-agent workflow that decomposes operator duties into monitoring, planning, action synthesis, simulation, validation, and reprompting; (ii) a Digital Process Plant Twin (DPPT) that exposes plant data, models, and a simulation service for pre-execution testing; and (iii) a Graph Retrieval-Augmented Generation (Graph RAG) layer built on the CPSMod ontology, which organizes plant knowledge (structure, function, hybrid dynamics, control context, and ...
| Authors | Javal Vyas, Milapji Singh Gill, Artan Markaj et al. |
|---|---|
| Primary category | eess.SY |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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