Robust tuning of PID controllers via uncertainty model identification

1997 ◽  
Author(s):  
M. Canale ◽  
G. Fiorio ◽  
S. Malan ◽  
M. Milanese ◽  
M. Taragna
Keyword(s):  
Model Identification ◽  
Pid Controllers ◽  
Uncertainty Model

Robust Tuning of Low-Order Controllers via Uncertainty Model Identification

1999 ◽  
Vol 5 (2-4) ◽  
pp. 316-328 ◽  
Author(s):  
M. Canale ◽  
G. Fiorio ◽  
S. Malan ◽  
M. Taragna
Keyword(s):  
Model Identification ◽  
Uncertainty Model ◽  
Low Order

Robust Design of Low Order Controllers via Uncertainty Model Identification

1997 ◽  
Vol 30 (16) ◽  
pp. 45-50 ◽  
Author(s):  
G. Fiorio ◽  
S. Malan ◽  
M. Milanese ◽  
M. Taragna
Keyword(s):  
Robust Design ◽  
Model Identification ◽  
Uncertainty Model ◽  
Low Order

Causal uncertainty model revisited

2008 ◽  
Author(s):  
Stephanie Tobin ◽  
John Edwards ◽  
Gifford Weary
Keyword(s):  
Causal Uncertainty ◽  
Uncertainty Model

Development of a Jacobian Module for Advanced Pulp and Paper Simulation and Control

TAPPI Journal ◽  
2009 ◽  
Vol 8 (1) ◽  
pp. 4-11
Author(s):  
MOHAMED CHBEL ◽  
LUC LAPERRIÈRE
Keyword(s):  
Control System ◽  
Nonlinear Behavior ◽  
Simulation Software ◽  
Pulp And Paper ◽  
Pid Controllers ◽  
Tuning Parameters ◽  
Pid Tuning ◽  
Fixed Set ◽  
And Control ◽  
Operating Points

Pulp and paper processes frequently present nonlinear behavior, which means that process dynam-ics change with the operating points. These nonlinearities can challenge process control. PID controllers are the most popular controllers because they are simple and robust. However, a fixed set of PID tuning parameters is gen-erally not sufficient to optimize control of the process. Problems related to nonlinearities such as sluggish or oscilla-tory response can arise in different operating regions. Gain scheduling is a potential solution. In processes with mul-tiple control objectives, the control strategy must further evaluate loop interactions to decide on the pairing of manipulated and controlled variables that minimize the effect of such interactions and hence, optimize controller’s performance and stability. Using the CADSIM Plus™ commercial simulation software, we developed a Jacobian sim-ulation module that enables automatic bumps on the manipulated variables to calculate process gains at different operating points. These gains can be used in controller tuning. The module also enables the control system designer to evaluate loop interactions in a multivariable control system by calculating the Relative Gain Array (RGA) matrix, of which the Jacobian is an essential part.

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