Summary ChatGPT for Control Logic Generation in PLCDCS arxiv.org
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One Line
The paper explores the potential of using large language models for generating control logic in PLC/DCS systems through an exploratory study and the creation of a collection for experimentation.
Slides
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Key Points
- Large language models (LLMs) are being explored for control logic generation in PLC/DCS systems.
- The effectiveness of LLMs in control logic programming is still unknown.
- ChatGPT, an AI language model, has been used for control logic generation in PLC/DCS systems.
- ChatGPT can generate efficient algorithms and use standard methods for various control tasks.
- Prompt granularity is crucial for generating high-quality code with ChatGPT.
Summaries
38 word summary
This paper examines the use of large language models (LLMs) for control logic generation in PLC/DCS systems. An exploratory study was conducted to assess the potential of LLMs in assisting control engineers. A collection was created for experimentation.
39 word summary
This paper explores the use of large language models (LLMs) for control logic generation in PLC/DCS systems. The authors conducted an exploratory study to determine how LLMs can support control engineers in creating control logic. They created a collection
383 word summary
This paper explores the use of large language models (LLMs) for control logic generation in PLC/DCS systems. While LLMs have been successful in generating source code for general purpose programming languages, their effectiveness in control logic programming is still unknown
This excerpt discusses the use of language models (LLMs) in generating control logic for industrial automation applications. The authors conducted an exploratory study to determine how LLMs can support control engineers in creating control logic. They created a collection of 100
The document discusses the use of ChatGPT for control logic generation in PLCDCS. It highlights different categories of prompts used to test the capabilities of ChatGPT. These categories include interlocks, diagnostics and communication, advanced process control, various engineering
The document discusses the use of ChatGPT for control logic generation in PLCDCS. It presents a table with 100 prompts for this purpose, categorized into 10 representative categories.
The text excerpt appears to be a series of numbers and unrelated information that is difficult to decipher or summarize. It does not provide any coherent or meaningful information.
The document discusses the use of ChatGPT, an AI language model, for generating control logic in PLCDCS. The AI model demonstrated the ability to generate efficient algorithms and use standard methods for various control tasks such as counter, temperature control, timer
ChatGPT has demonstrated the ability to generate larger programs in a top-down manner. It can retrieve domain knowledge on specific recipes and machines, as well as generate interlocks and diagnostics/communication code. ChatGPT can also generate complex dynamic models and
ChatGPT demonstrates technical knowledge in communication protocols and the ST language but has limitations in accessing non-public, vendor-specific data. Prompt granularity is crucial for generating high-quality code. ChatGPT can be used for prompt engineering, but it may be more
This excerpt contains a list of references to various research papers and articles related to code generation in industrial automation systems. The references include topics such as pretrained foundation models, the impact of AI on developer productivity, competition-level code generation, automated control code generation,
This excerpt includes a list of references to various research papers and articles related to the use of automated generation of control logic in PLCDCS systems. The references cover topics such as code generation in industrial automation, code suggestions in GitHub Copilot, failures of