Rockwell Automation Technologies Incorporated

W Douglas Johnson

L Paul Collette, Iii

Bijan Sayyarrodsari

Eric J Hartman

Carl Anthony Schweiger

System and method for controlling a process with spatially dependent conditions for producing a product with spatially dependent properties, e.g., a web/sheet-based process for producing a web/sheet-based product. Input data comprising a plurality of input data sets are provided to a neural network (analog or computer-based), each data set comprising values for one or more input parameters, each comprising a respective process condition or product property. The input data preserve spatial relationships of the input data. The neural network generates output data in accordance with the input data, the output data comprising a plurality of output data sets, each comprising values for one or more output parameters, each comprising a predicted process condition or product property. The output data preserve spatial relationships of the output data, which correspond to the spatial relationships of the input data. The output data are useable by a controller or operator to control the process.

Process control system using spatially dependent data for controlling a web-based process

System and method for training a support vector machine (SVM) with process constraints. A model (primal or dual formulation) implemented with an SVM and representing a plant or process with one or more known attributes is provided. One or more process constraints that correspond to the one or more known attributes are specified, and the model trained subject to the one or more process constraints. The model includes one or more inputs and one or more outputs, as well as one or more gains, each a respective partial derivative of an output with respect to a respective input. The process constraints may include any of: one or more gain constraints, each corresponding to a respective gain; one or more Nth order gain constraints; one or more input constraints; and/or one or more output constraints. The trained model may then be used to control or manage the plant or process.

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Austin, TX, United States of America

W Douglas Johnson