Siemens Aktiengesellschaft

Siemens Corporation

Siemens Industry, Inc.

Kun Ji

Zhen Song

Dong Wei

Yan Lu

George Lo

Linxia Liao

Ayman Fawaz

Kurt Dirk Bettenhausen

Florin Darie

An intelligent workpiece system includes a workpiece comprising a portion of a product; and an embedded computing system attached to the workpiece. The embedded computing system is configured to communicate with machines in a manufacturing environment to facilitate assembly of the workpiece into the product at a plurality of assembling areas.

Intelligent workpieces

Technical solutions are described for tuning a building control system, such as a variable air volume system, which includes intervened control loops. In one aspect, a method includes receiving a performance objective of the system. The method also includes generating a collection of values for parameters of the control loops based on stored information that includes a stored velocity and a stored position. The method also includes evaluating the collection of values for the parameters by comparing a performance measure and the performance objective of the system. In response to a difference between the performance objective and the performance measure satisfying a predetermined threshold the collection of values are stored and the control loops are configured according to the stored information for the parameters. The present document further describes examples of other aspects such as systems, computer products.

Tuning building control systems

A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S). A future condition of each of the plurality of subsystems is estimated from the received operational data (S). A control strategy for delaying a need for system maintenance is generated based on the estimated future condition of each of the plurality of subsystems (S). An operation of the system is controlled based on the generated control strategy (S).

Resilient optimization and control for distributed systems

A method of controlling energy consumption in a building includes receiving occupancy data including at least one of occupant request data and occupant schedule data, receiving weather data including at least one of current weather measurement data and weather forecast data, generating an output control signal based on the occupancy data and the weather data, and transmitting the output control signal to a building automation system (BAS) of the building. The occupant request data includes a current request, and the occupant schedule data includes a predicted occupant schedule. The output control signal adjusts a building control device in a zone in the building.

System and method of energy management control

A distributed cyber-physical system includes physical elements (---n) disposed in a physical space (). Controllers (---n) are disposed in a cyber space (). Each of the physical elements (---n) corresponds to a corresponding controller (---n). A cyber infrastructure () is disposed in the cyber space (). The cyber infrastructure () manages a connection between the controllers (---n) and the physical elements (---n). Control loops () are established via the cyber infrastructure (). Each of the control loops () includes a physical element and a corresponding controller. A resilient control agent () is configured to monitor each of the control loops (), determine when one of the control loops () is experiencing an adverse condition, reduce a sampling frequency of the control loops () that are not experiencing the adverse condition, and increase a sampling frequency of the control loop () that is experiencing the adverse condition.

Resilient control design for distributed cyber-physical systems

A method including: computing a total cost for a first zone in a building, wherein the total cost is equal to an actual energy cost of the first zone plus a thermal discomfort cost of the first zone; and heuristically optimizing the total cost to identify temperature setpoints for a mechanical heating/cooling system and a start time and an end time of the mechanical heating/cooling system, based on external weather data and occupancy data of the first zone.

Cost optimization for buildings with hybrid ventilation systems

A method of controlling energy consumption in a building. The method includes receiving occupant request data including a plurality of requests, wherein each of the plurality of requests corresponds to one of a plurality of zones in the building wherein the occupant request data is received via a cloud computing resource. The method also includes receiving weather data including at least one of current weather measurement data and weather forecast data wherein the weather data is received via a cloud computing resource. In addition, a facility management rule is received via a cloud computing resource. Further, the method includes generating a plurality of output control signals via cloud computing resource, wherein each of the plurality of output control signals is based on one of a plurality of requests and predicted occupant schedules, energy price data and the facility management rule. The control signals are generated by using simulation-based model predictive control method to determine a set of optimized control signals based on optimized energy use or optimized energy cost. The optimized control signals are transmitted to controllers.

Cloud enabled building automation system

A Model Predictive Control (MPC) framework is implemented as part of the run-time system function features of a Programmable Logic Controller (PLC) system. Optimal control calculations are performed in the run-time MPC function block of the PLC. The optimal control function is determined by an MPC block in an engineering tool of the PLC, using a system dynamic matrix containing measurements from a unit step response test performed by the PLC.

System and method for implementing model predictive control in PLC

A model predictive control (MPC) framework is enhanced to provide prognostic information and fault tolerant control strategies. A system fault is calculated based on predicted values and is used to generate an internal reference trajectory to which the system is controlled using MPC. If the system fault exceeds a threshold maximum, then the MPC control model is either tuned or reconfigured depending on whether the fault is external or internal.

System and method for fault prognostics enhanced MPC framework

A method and system may operate to receive a first data from a sensor of a controlled device via a wireless network at a first time, transmit the first data to a controller system, receive a second data from the sensor of the controlled device via the wireless network at a second time, transmit the second data to the controller system, determine that a third data has not been received prior to a predetermined time period, or that the third data has been received prior to the predetermined time period and the third data is outside of a determined range of values, calculate a fourth data based on the first data and the second data, and transmit the fourth data to the controller system at a third time.

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Plainsboro, NJ, United States of America

Kun Ji