Proportional-integral controller retuning to improve controller performance in formaldehyde production process at PT X

2020 ◽  
Author(s):  
Abdul Wahid ◽  
Dyah Kunti Surya Andari
Keyword(s):  
Production Process ◽  
Proportional Integral ◽  
Controller Performance ◽  
Integral Controller ◽  
Proportional Integral Controller ◽  
Formaldehyde Production

Proportional-Integral Controller Assisted by GPI Observer for Microalgal Continuous Culture

2020 ◽  
Author(s):  
Viyils Sangregorio-Soto ◽  
Claudia L. Garzon-Castro ◽  
Gianfranco Mazzanti ◽  
Manuel Figueredo ◽  
John A. Cortes-Romero
Keyword(s):  
Continuous Culture ◽  
Proportional Integral ◽  
Integral Controller ◽  
Proportional Integral Controller

Limits of a simplified controller design based on integral plus dead time models

2018 ◽  
Vol 232 (6) ◽  
pp. 728-741 ◽  
Author(s):  
Mikuláš Huba ◽  
Igor Bélai
Keyword(s):  
Dead Time ◽  
Controller Design ◽  
Absolute Error ◽  
Higher Order ◽  
Smith Predictor ◽  
Proportional Integral ◽  
Overall Design ◽  
Error Measures ◽  
Integral Controller ◽  
Proportional Integral Controller

This article presents design and evaluation of filtered proportional–integral controllers and filtered Smith predictor–inspired constrained dead time compensators. Both are based on the integral plus dead time and on the first-order time delayed plant models. They are compared as for tuning simplicity, robustness and noise attenuation. Such a comparison, which presents a robustness test regarding the importance of the internal plant feedback approximation, may be carried out by performance measures built on deviations of the input and output transient responses from their ideal shapes. When combined with integral of absolute error measures of both solution types with the disturbance responses set as nearly equivalent, we can see that the filtered Smith predictor setpoint responses may be significantly faster than the filtered proportional–integral controller responses, more robust and, using higher-order filters, also sufficiently smooth. Furthermore, tuning of the possibly higher-order filters for filtered Smith predictor is simpler. Its overall design is more transparent and straightforward with respect to the control constraints, where the filtered Smith predictor requires some additional anti-windup measures.

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