Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Method for operating a communication network that includes a communication devices and form part of an industrial automation system, wherein control units control functions of associated communication devices, where a prescribable proportion of system resources of an associated communication device is provide for a prescribable resource use duration for each control unit, when prescribable synchronization events occur, the control units synchronously detect state variables of the communication devices and adjust them to one another, the control units additionally determine, for at least one past resource use duration, how a determination time available for a path determination influences quality criterion changes for communication network paths to be determined, and determine a first correction value synchronization events based on the quality criterion changes, and the control units determine a second correction value for the synchronization events if inconsistent state variables are determined when the state variables are adjusted to one another.

In a combination of diverse devices, system control with high real-time property is realized. A control device includes: a control indication acceptor receiving a control target indication to be a target of control with respect to plural controlled devices; and a control processing generator generating a control signal with respect to each of the plural controlled devices based on the control target indication, in which operations of the plural controlled devices are controlled so that the plural controlled devices cooperate to achieve the target, based on at least one of a communication delay time, a phase shift time, and an operation cycle time of the plural controlled devices.

Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Method for operating a communication network that includes a communication devices and form part of an industrial automation system, wherein control units control functions of associated communication devices, where a prescribable proportion of system resources of an associated communication device is provide for a prescribable resource use duration for each control unit, when prescribable synchronization events occur, the control units synchronously detect state variables of the communication devices and adjust them to one another, the control units additionally determine, for at least one past resource use duration, how a determination time available for a path determination influences quality criterion changes for communication network paths to be determined, and determine a first correction value synchronization events based on the quality criterion changes, and the control units determine a second correction value for the synchronization events if inconsistent state variables are determined when the state variables are adjusted to one another.

A control device includes a timing detection unit, a setpoint path generation unit, and a control computation unit. The timing detection unit detects a timing at which an event indicating a change in a target setpoint or an event indicating application of a disturbance occurs, as a generation start timing at which generation of a path of a generation setpoint is started. The setpoint path generation unit determines the path of the generation setpoint in response to determination of the generation start timing at which generation of the path of the generation setpoint is started and outputs, in every control cycle, the generation setpoint that follows the determined path. In every control cycle, the control computation unit calculates a manipulated variable by performing control computation using a process variable and the generation setpoint as input values.

A simulator linkage device (10) makes a first simulator (300) and a second simulator (400) execute simulation periodically respectively at a sampling interval set by the user.