Abstract: A cutting tool includes a trunk assembly, an electric motor, a second housing for accommodating the electric motor, an output shaft for mounting a cutting piece, a transmission assembly connected between a motor shaft and the output shaft, and a third housing for accommodating at least part of the transmission assembly. In a process of the cutting piece cutting a workpiece, the third housing has a first contact surface and a second contact surface with the workpiece; when the first contact surface or the second contact surface is in contact with the workpiece, the cutting piece has a maximum cutting depth; and the angle between the first contact surface and the second contact surface is greater than or equal to 50° and less than or equal to 85°, and the maximum cutting depth is greater than or equal to 20 mm.

Abstract: 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.

Abstract: 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.

Abstract: A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S21). A future condition of each of the plurality of subsystems is estimated from the received operational data (S22). 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 (S23). An operation of the system is controlled based on the generated control strategy (S24).

Abstract: 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.

Abstract: 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.

Abstract: A distributed cyber-physical system includes physical elements (15-1, 15-2, . . . , 15-n) disposed in a physical space (11). Controllers (13-1, 13-2, . . . , 13-n) are disposed in a cyber space (10). Each of the physical elements (15-1, 15-2, . . . , 15-n) corresponds to a corresponding controller (13-1, 13-2, . . . , 13-n). A cyber infrastructure (12) is disposed in the cyber space (10). The cyber infrastructure (12) manages a connection between the controllers (13-1, 13-2, . . . , 13-n) and the physical elements (15-1, 15-2, . . . , 15-n). Control loops (14) are established via the cyber infrastructure (12). Each of the control loops (14) includes a physical element and a corresponding controller.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The safety and integrity of an embedded computer system is monitored using an independent safety monitoring module in communication with the main controller module via a serial connection to a safety monitoring module proxy in the main controller module. The main controller module is monitored through the use of alive-telegram exchanges and computational challenges. The safety monitoring module also receives temperature information and supply voltage information about the main controller module. The monitored information may be evaluated using a prognostic model constructed using a simulation of failure modes off line.

Abstract: Real-time programmable logic controller software is executed on a multicore processor system. An organizational block executer is executed on a first core, and system service functions associated with the programmable logic controller software are executed on the second core. The organizational block executer includes I/O scanning and logic solving in a single-threaded execution. Core-switch operations may be cyclically performed to move execution of the organizational block executer from the first core to the second core and back to the first core.

Abstract: A distributed cyber-physical system includes physical elements (15-1, 15-2, . . . , 15-n) disposed in a physical space (11). Controllers (13-1, 13-2, . . . , 13-n) are disposed in a cyber space (10). Each of the physical elements (15-1, 15-2, . . . , 15-n) corresponds to a corresponding controller (13-1, 13-2, . . . , 13-n). A cyber infrastructure (12) is disposed in the cyber space (10). The cyber infrastructure (12) manages a connection between the controllers (13-1, 13-2, . . . , 13-n) and the physical elements (15-1, 15-2, . . . , 15-n). Control loops (14) are established via the cyber infrastructure (12). Each of the control loops (14) includes a physical element and a corresponding controller.

Abstract: A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S21). A future condition of each of the plurality of subsystems is estimated from the received operational data (S22). 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 (S23). An operation of the system is controlled based on the generated control strategy (S24).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of controlling energy consumption in a building. The method includes receiving occupant request data comprising 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 comprising 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.

Abstract: 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.