CONTROLLING A NON-LINEAR PROCESS

• US 20080208778A1
• Filed: 04/30/2008
• Published: 08/28/2008
• Est. Priority Date: 12/09/2002
• Status: Abandoned Application

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Plumer, Edward, Hartman, Eric, Sayyar-Rodsari, Bijan, Liano, Kadir, Axelrud, Celson
• Application Number:
  US12/112,847
• Publication Number:
  US 20080208778A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 17/02 electric

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.

Claims

1. A method for controlling a nonlinear process, the method comprising:

a) initializing a parametric universal nonlinear dynamic approximator to a current status of the nonlinear process, comprising process inputs and outputs, said initializing comprising;

initializing inputs to a nonlinear approximator comprised in the parametric universal nonlinear dynamic approximator, wherein the nonlinear approximator is trained to model dependencies of one or more parameters of a parameterized dynamic model of the nonlinear process comprised in the parametric universal nonlinear dynamic approximator upon operating conditions of the nonlinear process;

executing the trained nonlinear approximator to determine initial values for the one or more parameters of the parameterized dynamic model based on the current status of the nonlinear process; and

initializing the parameterized dynamic model with the determined values for the one or more parameters;

b) formulating an optimization problem, including specifying an objective for the nonlinear process;

c) generating a profile of manipulated variables for the nonlinear process over a control horizon in accordance with the specified objective for the nonlinear process;

d) operating the parametric universal nonlinear dynamic approximator in accordance with the generated profile of manipulated variables, thereby generating predicted outputs for the nonlinear process;

e) determining a deviation of the predicted outputs from a desired behavior of the nonlinear process;

f) repeating b)-e) one or more times to determine an optimal profile of manipulated variables in accordance with the specified objective for the nonlinear process;

g) operating the nonlinear process in accordance with the optimal profile of manipulated variables, thereby generating process output; and

repeating a)-g) one or more times to dynamically control the nonlinear process.

2. The method of claim 1, further comprising:

h) modifying the optimization problem based on the input to the model;

wherein said repeating a)-g) comprises repeating a)-h).

3. The method of claim 2, wherein said modifying the optimization problem comprises modifying one or more of:

constraints;

an objective function;

model parameters;

optimization parameters; and

optimization data.

4. The method of claim 1, wherein the nonlinear approximator comprises one or more of:

a neural network;

a support vector machine;

a statistical model;

a parametric description of the nonlinear process;

a Fourier series model;

or

an empirical model.

5. The method of claim 1, wherein the nonlinear approximator comprises a universal nonlinear approximator.

6. The memory medium of claim 1, wherein the nonlinear approximator includes a feedback loop, and wherein the feedback loop is operable to provide output of the nonlinear approximator from a previous cycle as input to the nonlinear approximator for a current cycle.

7. The method of claim 1, wherein the parameterized dynamic model comprises a multi-input, multi-output (MIMO) dynamic model implemented with a set of difference equations.

11. The method of claim 1, wherein said repeating a)-g) one or more times to dynamically control the nonlinear process further comprises:

adapatively training the parametric universal nonlinear dynamic approximator in an iterative manner.

9. The method of claim 9, wherein the one or more process inputs are received from one or more of:

the nonlinear process;

or

a representation of the nonlinear process.

8. The method of claim 9, wherein the set of difference equations comprises a set of discrete time polynomials.

10. The method of claim 9, wherein the representation of the nonlinear process comprises one or more of:

a first principles model;

a statistical model;

a parametric description of the nonlinear process;

a Fourier series model;

an empirical model;

or

empirical data.

12. A computer accessible memory medium that stores program instructions for controlling a nonlinear process, wherein the program instructions are executable by a processor to perform:

a) initializing a parametric universal nonlinear dynamic approximator to a current status of the nonlinear process, comprising process inputs and outputs, said initializing comprising;

initializing inputs to a nonlinear approximator comprised in the parametric universal nonlinear dynamic approximator, wherein the nonlinear approximator is trained to model dependencies of one or more parameters of a parameterized dynamic model of the nonlinear process comprised in the parametric universal nonlinear dynamic approximator upon operating conditions of the nonlinear process;

executing the trained nonlinear approximator to determine initial values for the one or more parameters of the parameterized dynamic model based on the current status of the nonlinear process; and

initializing the parameterized dynamic model with the determined values for the one or more parameters;

b) formulating an optimization problem, including specifying an objective for the nonlinear process;

c) generating a profile of manipulated variables for the nonlinear process over a control horizon in accordance with the specified objective for the nonlinear process;

d) operating the parametric universal nonlinear dynamic approximator in accordance with the generated profile of manipulated variables, thereby generating predicted outputs for the nonlinear process;

e) determining a deviation of the predicted outputs from a desired behavior of the nonlinear process;

f) repeating b)-e) one or more times to determine an optimal profile of manipulated variables in accordance with the specified objective for the nonlinear process;

g) operating the nonlinear process in accordance with the optimal profile of manipulated variables, thereby generating process output; and

repeating a)-g) one or more times to dynamically control the nonlinear process.

13. The memory medium of claim 12, wherein the program instructions are further executable to perform:

h) modifying the optimization problem based on the input to the model;

wherein said repeating a)-g) comprises repeating a)-h).

14. The memory medium of claim 13, wherein said modifying the optimization problem comprises modifying one or more of:

constraints;

an objective function;

model parameters;

optimization parameters; and

optimization data.

15. A system for controlling a nonlinear process, the system comprising:

means for a) initializing a parametric universal nonlinear dynamic approximator to a current status of the nonlinear process, comprising process inputs and outputs, comprising;

means for initializing inputs to a nonlinear approximator comprised in the parametric universal nonlinear dynamic approximator, wherein the nonlinear approximator is trained to model dependencies of one or more parameters of a parameterized dynamic model of the nonlinear process comprised in the parametric universal nonlinear dynamic approximator upon operating conditions of the nonlinear process;

means for executing the trained nonlinear approximator to determine initial values for the one or more parameters of the parameterized dynamic model based on the current status of the nonlinear process;

means for initializing the parameterized dynamic model with the determined values for the one or more parameters;

means for b) formulating an optimization problem, including specifying an objective for the nonlinear process;

means for c) generating a profile of manipulated variables for the nonlinear process over a control horizon in accordance with the specified objective for the nonlinear process;

means for d) operating the parametric universal nonlinear dynamic approximator in accordance with the generated profile of manipulated variables, thereby generating predicted outputs for the nonlinear process;

means for e) determining a deviation of the predicted outputs from a desired behavior of the nonlinear process;

means for f) repeating b)-e) one or more times to determine an optimal profile of manipulated variables in accordance with the specified objective for the nonlinear process;

means for g) operating the nonlinear process in accordance with the optimal profile of manipulated variables, thereby generating process output; and

means for repeating a)-g) one or more times to dynamically control the nonlinear process.