Rockwell Automation

The present technology relates to clamping breaking currents in a circuit breaker to protect solid-state switches of the circuit breaker during an overload event. In an embodiment, a circuit breaker includes processing circuitry and switching circuitry coupled to the processing circuitry. The switching circuitry includes a first switch coupled to an AC voltage supply, a second switch, and the processing circuitry, a second switch coupled to the first switch, a load, and the processing circuitry, a first current clamping circuit coupled to the AC voltage supply, the second switch, and the processing circuitry, and a second current clamping circuit coupled to the AC voltage supply the first switch, and the processing circuitry. The processing circuitry monitors current at the load and provides gating signals to the first and second switches and the first and second current clamping circuits to control current flow between the AC voltage supply and the load.

An integrated development environment (IDE) for uses a generative artificial intelligence (AI) model to generate industrial control code in accordance with functional requirements provided to the industrial IDE system as natural language prompts. The system's generative AI model leverages both a code repository storing sample control code and a document repository that stores device or software manuals, program instruction manuals, functional specification documents, or other technical documents. These repositories are synchronized by digitizing selected portions of document text from the document repository into control code for storage in the code repository, as well as contextualizing control code from the code repository into text-based documentation for storage in the document repository.

A method of controlling movement of a mover along a track of a linear drive system includes obtaining track data from simulated or actual movement of the mover along the track. The method includes determining, based on the track data, a value of angular misalignment between at least one drive coil of the track and a magnet array of the mover due to curvature of one of multiple track sections. The method also includes determining, based on the value of angular misalignment, a correction for a control of at least one of the drive coils. The method also includes operating the at least one of the drive coils according to the correction for the control to compensate for the angular misalignment.

A system, comprising: a memory that stores executable components; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a device interface component (210) configured to collect runtime data from industrial assets in operation within an industrial facility; a generative artificial intelligence (Al) component (208) configured to, based on analysis of the runtime data and a response prompted from a generative Al model, predict an occurrence of anomalous operation by one or more of the industrial assets, and formulate a corresponding recommended action predicted to mitigate the occurrence of the anomalous operation; and a user interface component (204) configured to render, on a client device, the recommended action as a natural language recommendation.

The present technology relates to a modular industrial automation system that is compliant with 4B separation standards from the International Electrotechnical Commission (IEC) for standard number 61439. In particular, when modular industrial automation systems are implemented, the terminals in the electrical cabling cabinet must be properly electrically isolated to meet requirements. However, due to space limitations, the addition of multiple components horizontally within the system creates horizontal access and isolation issues. Further, due to the space limitations, physically accessing the terminals becomes challenging. As yet another challenge, the cabinet may suffer from overheating issues due to the size of the space and the configuration of the terminals. Finally, to be fully compliant, each terminal within the cabinet must be compliant. Accordingly, the design disclosed allows for avoiding overheating while meeting the requirements and providing accessible terminals.

A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.

A non-transitory, tangible, computer readable medium including instructions for generating industrial automation projects that, when executed by processing circuitry, causes the processing circuitry to perform operations including requesting a plurality of documents associated with an industrial automation system, receiving one or more documents of the plurality of documents, extracting contextual data from the one or more documents, and generating an industrial automation project file by applying machine learning techniques on the contextual data, where the industrial automation system is configured to perform an industrial automation process based on the industrial automation project.

A system for generating a human-machine interface (HMI) from control code having standard objects. Control code is generated from a model of an automation system suitable for generating a three-dimensional rendering of the automation system and simulating the automation system. The model may also be used for is used for SIL and HIL testing. Standard objects form the control code are used to generate an HMI that includes status indicators for an actuator and control elements for an actuator. The generated HMI is linked to the data sources for the status indicators and the actuator controlled by the control elements. Interacting with the HMI causes a change in the behavior (e.g., position, etc.) of the actuators and results in affecting a condition of the process control environment.

Described systems and methods may enable control of equipment using a response generated by an LLM and a model for the equipment. The techniques may include receiving a prompt related to an operation of an industrial automation equipment. The techniques may also include providing the prompt as an input to a Large Language Model (LLM) that stores information related to the industrial automation equipment. Further, the techniques include retrieving identifier information as an output of the LLM. Further still, the techniques include determining that a process parameter related to the operation of the industrial automation equipment is accessible by the computing system based on the identifier information. Further still, the techniques include obtaining a model for the operation based on the process parameter being accessible, generating a response to the prompt based on the model, controlling the industrial automation equipment based on the response.

A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.