Automatic Generation of PLC Control Code from Natural Language Requirement Specifications

Developing control programs for manufacturing systems is time-consuming and requires expert control designers.While manual programming is common, it becomes complex as systems grow, leading to long development times, frequent errors, and difficult maintenance.To address these issues, researchers have introduced formal methods like Supervisory Control Theory (SCT) and model checking to improve precision and verification.Although these are some of the most advanced approaches, they are difficult to use in practice because they are time-consuming, require high mathematical expertise, and face scalability problems such as combinatorial explosion in large systems.This study aims to overcome these limitations by presenting an AI-based system that automatically generates programmable logic controller (PLC) code from natural language requirement specifications.The approach uses AutoFactory, a dataset of annotated specifications, and fine-tunes two Bidirectional Encoder Representations from Transformers (BERT)-based models to extract actuators, pre-actuators, and sensors before generating International Electrotechnical Commission (IEC) 61131-3 Structured Text (ST) code.BERT-Base achieved an F1 score of 0.9711, showing reliable component extraction.The study proves that transformer models can accurately detect control components and initiate logic generation.These results confirm that AI can assist and augment control designers by automating extraction and initial coding.Future work will complete the pipeline to deliver verified IEC 61131-3 code ready for industrial deployment.