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1. US20160091873 - Process control method

Office
United States of America
Application Number 14892766
Application Date 15.05.2014
Publication Number 20160091873
Publication Date 31.03.2016
Grant Number 10901378
Grant Date 26.01.2021
Publication Kind B2
IPC
G05B 13/04
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
04involving the use of models or simulators
CPC
G05B 13/042
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
04involving the use of models or simulators
042in which a parameter or coefficient is automatically adjusted to optimise the performance
G05B 13/048
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
04involving the use of models or simulators
048using a predictor
Applicants Core A/S
Inventors Preben Alstrom
Agents Jones Robb, PLLC
Title
(EN) Process control method
Abstract
(EN)

The invention relates to method for controlling a process, the method comprising an adaptive control model and at least one process input and at least one process output, the control model comprising predicting the relevant targets in the process; and selecting the relevant drivers for the process based on the target prediction, where the method preferably comprises adapting a number of parameters based on one or more inputs, and using the adapted parameters as an input for the target prediction.