A multiple-input/multiple-output control routine in the form of a model predictive control (MPC) routine operates with wireless or other sensors that provide non-periodic, intermittent or otherwise delayed process variable measurement signals at an effective rate that is slower than the MPC controller scan or execution rate. The wireless MPC routine operates normally even when the measurement scan period for the controlled process variables is significantly larger than the operational scan period of the MPC controller routine, while providing control signals that enable control of the process in a robust and acceptable manner. During operation, the MPC routine uses an internal process model to simulate one or more measured process parameter values without performing model bias correction during the scan periods at which no new process parameter measurements are transmitted to the controller. When a new measurement for a particular process variable is available at the controller, the model prediction and simulated parameter values are updated with model bias correction based on the new measurement value, according to traditional MPC techniques.

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 method includes retrieving process variable values from a multivariable predictive controller. The multivariable predictive controller is executed on an embedded platform and is configured to control an industrial process. The method also includes simulating how the multivariable predictive controller would attempt to control the industrial process based on the process variable values. The method further includes transmitting simulated process variable values to the multivariable predictive controller.

A computer implemented method for controlling a finite set of components which are fluidly connected to a common compressed air distribution system includes iteratively repeating the steps of: receiving prediction data for said compressed air distribution system; receiving characterising data for each component of said set of components; determining one or more sets of continuously differentiable functions, wherein each of said sets of functions represents a unique sequence of operation of the components in said set of components; selecting an optimal set of functions from said one or more sets of continuously differentiable functions; wherein the unique sequence of operation represented by said optimal set meets said prediction data; deriving configuration data for said set of components from said optimal set of functions; configuring each component of said set of components based on said configuration data.

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.

This disclosure relates to system and method to reconstruct human motion for mobile robot teleoperation using shared control. The method of the present disclosure acquire an input feed of a human operator to perform a task with assistance in a remote environment using shared control. The mobile robot reconstructs to follow a trajectory of the human operator towards the intended goal in the remote environment. The mobile robot determines at least one goal intended by the human operator based on a previously occurred state, a current kinematic state and a future trajectory of the human operator and a known position of the plurality of goals. The model predictive control generates at least one instruction to control the movement of the mobile robot to perform at least one of following the trajectory of the human operator and reaching the operator intended goal based on a joint angle position and a velocity.

A state-space model which includes one or more neural networks. The state-space model is configured to stochastically model a technical system by modelling uncertainties both in latent states of the technical system and in weights of the one or more neural networks. Thereby, the state-space model may be able to capture both aleatoric uncertainty (inherent unpredictability in observations) and epistemic uncertainty (uncertainty in the model's parameters or weights. During the training and during subsequent use for model-predictive control, moment matching across neural network layers is used, which may ensure that the model's predictions are consistent and close to real system behavior.

In one aspect of the present disclosure, a method includes obtaining, by a processing device, input data indicative of a first set of process parameters. The method further includes providing the input data to a first process model. The method further includes obtaining, from the first process model, first predictive output indicative of performance of a first process operation in accordance with the first set of process parameters. The method further includes providing the first predictive output to a second process model. The method further includes obtaining, from the second process model, second predictive output indicative of performance of a second process operation, different than the first process operation or a repetition of the first process operation, in accordance with the first set of process parameters. The method further includes performing a corrective action in view of the second predictive output.

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 system and method for the automated checking of I/O loops of a process automation system is disclosed that includes a dongle configured to be installed on a terminal block and make an electrical connection to at least one I/O loop. Operating software communicates with the dongle and to a database of I/O loop data. The operating software constructs an I/O loop check file using the database of I/O loop data and downloads the I/O loop check file to the dongle, where the dongle uses the I/O loop check file to test the I/O loop.