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.

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.

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.

A control device, a method of controlling the control device and recording medium are provided. An adherence performance of all of a plurality of servo control systems is improved. A controller predicts a response of a first servo control system corresponding to a corrected trajectory and corrects a first command value or generates a second inverse kinematics trajectory using the predicted response.

A control device generates a second command value by compensating a first command value output at every control cycle according to a predetermined pattern with a correction amount output at every control cycle according to correction data, updates the correction data based on a deviation between the first command value and a feedback value from the control object, and determines an initial value of the correction data. The control device acquires a response characteristic indicating a relationship between an assigned command value and a feedback value shown in the control object in response to the command value, estimates a feedback value to be shown in the control object based on a value obtained by compensating the first command value with temporary correction data and the response characteristic, and updates the temporary correction data based on a deviation between the first command value and the estimated feedback value.

A method for controlling an actuator system of a motor vehicle includes utilizing a model predictive control (MPC) module with an MPC solver to determine optimal positions of a plurality of actuators subject to constraints, optimizing a cost function for a set of actuator duty cycles for controlling positions of the plurality of actuators, determining if the MPC solver has determined optimal actuator positions for the plurality of actuators, and applying a linear quadratic regulator (LQR) solution if the MPC solver fails to determine optimal actuator positions for the plurality of actuators.

A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.

A model predictive control (MPC) device includes an input interface configured to receive an industrial process input associated with at least one component of a process automation plant, an output interface configured to transmit a control instruction to control the component, memory configured to store first and second MPC process models corresponding to different states, and a processor configured to identify a current state parameter of an industrial process, and predict a future industrial process output using the first or second MPC process model, based on the current state parameter being associated with the first or second MPC process model. The processor is configured to calculate a target operating point according to the predicted future industrial process output, determine a control signal to drive the industrial process to the calculated target operating point, and output the determined control signal to control operation of the component of the industrial process plant.

A method for controlling an actuator system of a motor vehicle includes utilizing a model predictive control (MPC) module with an MPC solver to determine optimal positions of a plurality of actuators subject to constraints, optimizing a cost function for a set of actuator duty cycles for controlling positions of the plurality of actuators, determining if the MPC solver has determined optimal actuator positions for the plurality of actuators, and applying a linear quadratic regulator (LQR) solution if the MPC solver fails to determine optimal actuator positions for the plurality of actuators.

A method and system for robot motion control using a model predictive control (MPC) technique including torque rate control and suppression of end tooling oscillation. An MPC module includes a robot dynamics model which inherently reflects response nonlinearities associated with changes in robot configuration, and an optimization solver having an objective function with a torque rate term and inequality constraints defining bounds on both torque and torque rate. The torque rate control in the MPC module provides an effective means of controlling jerk in robot joints, while accurately modeling robot dynamics as the robot changes configuration during a motion program. End tooling oscillation dynamics may also be included in the MPC objective function and constraints in order to automatically control end tooling vibration in the calculations of the MPC module.