公开(公告)号：US20150185717A1

公开(公告)日：2015-07-02

申请号：US14659003

申请日：2015-03-16

Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.

Inventor： Bijan Sayyar-Rodsari ,  Edward S. Plumer ,  Eric Hartman ,  Celso Axelrud ,  Kadir Liano

IPC: G05B13/04

CPC classification number: G05B13/048 ,  G05B13/042 ,  G05B17/02

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

Abstract translation: 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。

公开(公告)号：US11169494B2

公开(公告)日：2021-11-09

申请号：US14659003

申请日：2015-03-16

Applicant: ROCKWELL AUTOMATION TECHNOLOGIES, INC.

Inventor： Bijan Sayyar-Rodsari ,  Edward S Plumer ,  Eric Hartman ,  Celso Axelrud ,  Kadir Liano

IPC: G05B13/04 ,  G05B17/02

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.