There is provided a method for optimizing a manufacturing process of a new part. The method includes executing, by a system configured to drive the manufacturing process, a set of manufacturing functions. Executing these functions include receiving data associated with one or more field parts similar to the new part, and generating, based on the data, a forecast representative of a longevity of the one or more parts. The method further includes generating a digital thread forming a surrogate model for the new part, based on the forecast. Further, the method includes creating the set of manufacturing functions based on the surrogate model and manufacturing the new part according to the set of manufacturing functions.

In an approach to creating a press-fit force analysis, one or more computer processors retrieve a force press-fit data from a press-fit machine based on a press cycle. One or more computer processors calculate a deformation force of the press cycle based on the press-fit data and storing the deformation force. One or more computer processors create a predictive control model based on the deformation force and determine if a corrective action is required based on at least one of a raw material quality data, machine setting data, a completed lot quality data or the predictive control model. One or more computer processors determine if a corrective action is required and alert a downstream process to take the corrective action. One or more computer processors schedule a material kitting.

Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.

A control system for controlling an operation of a processing machine positioning a worktool according to a processing pattern to machine a workpiece. A memory to store a reference trajectory defined in a spatial domain by a sequence of points for positioning the worktool and defined in a time domain by a relative time for positioning the worktool on each point of the reference trajectory. A sensor to determine a state of the processing machine. A reference governor to iteratively process the reference trajectory over a receding horizon including multiple windows of points, and analytically update the relative time for positioning the worktool for some points of the reference trajectory within the receding horizon to satisfy constraints on the operation of the processing machine having the state. A controller to control the operation of the processing machine using control inputs causing the worktool to track the updated reference trajectory.

Systems and methods for implementing an economic model predictive control (EMPC) strategy in any resource-based system include an EMPC tool. The EMPC tool is configured to present user interfaces to a client device. The EMPC tool is further configured to receive first user input including resources and subplants associated with a central plant. The EMPC tool is also configured to receive second user input including sinks and connections between central plant equipment. The EMPC tool also includes a data model extender configured to extend a data model to define new entities and/or relationships specified by user input. The EMPC tool also includes a high level EMPC algorithm configured to generate an optimization problem and an asset allocator configured to solve the resource optimization problem in order to determine optimal control decisions used to operate the central plant.

Disclosed is a multi-model predictive control method for a Pichia pastoris fermentation process, including: dividing prior data into m training sample clusters by using a fuzzy C-means algorithm (FCM); obtaining, for each sample cluster, a corresponding prediction model by using a least squares support vector machine (LSSVM) and an improved particle swarm optimization method (IPSO); then, designing a corresponding predictive controller; and finally, calculating a deviation between an output of an object and an output of each sub-prediction model at each sampling time to establish a multi-model fusion predictive controller. According to the method, the adaptive ability of the model is improved and an actual state of a nonlinear system is described more accurately.

A control device, a method of controlling the control device and recording medium are provided. Ranges of movement of a plurality of servo control systems are effectively used. A controller generates a corrected trajectory in which a high frequency component is removed from a first inverse kinematics trajectory so that no phase delay occurs as a command trajectory of a first servo control system.

An aggregation layer subsystem, and method of operation thereof, for use with an architect subsystem and a plurality of edge processing devices in a distributed analytics system, wherein each edge processing device is adapted to monitor and control the operation of at least one monitored system according to a first analytic model, the aggregation layer subsystem comprising: a processor and memory, the memory containing instructions which, when executed by the processor, enables the aggregation layer subsystem to: receive a second analytic model from the architect subsystem, the second analytic model based on characteristics of at least one monitored system associated with at least one of the plurality of edge processing devices; receive monitored system information from each of the plurality of edge processing devices; and, provide control signals to the at least one monitored system, via one of the edge processing devices, according to the second analytic model in response to the monitored system information.

Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.