Improvement of linear distillation column control performance using fuzzy self-tuning PI controller

2020 ◽  
Author(s):  
Abdul Wahid ◽  
Shafira Anandita ◽  
Muhammad Fathi Fadlian ◽  
Albert Harazaki Mendrofa
Keyword(s):  
Distillation Column ◽  
Pi Controller ◽  
Control Performance ◽  
Self Tuning ◽  
Distillation Column Control

scholarly journals Reflux control of a laboratory distillation column via MPC-based reference governor

2017 ◽  
Vol 10 (2) ◽  
pp. 139-143 ◽  
Author(s):  
Martin Klaučo ◽  
Richard Valo ◽  
Ján Drgoňa
Keyword(s):  
Control Strategy ◽  
Distillation Column ◽  
Pi Controller ◽  
Laboratory Scale ◽  
Experimental Results ◽  
Reflux Ratio ◽  
Control Performance ◽  
Reference Governor

AbstractAn optimization-based control strategy is proposed to improve control performance of a primary PI controller. The strategy, referred to as a MPC-based reference governor, optimizes the performance of a primary PI controller by supplying optimal setpoints to the primary controller. This primary PI controller is responsible for reflux ratio manipulation in a distillation column, to control the temperature of the column head. This paper shows theoretical and experimental results obtained on the laboratory-scale.

Evaluation of Self-Tuning Controllers for Distillation Column Control

1980 ◽  
Vol 13 (9) ◽  
pp. 345-354
Author(s):  
A.J. Morris ◽  
Y. Nazer ◽  
R.K. Wood ◽  
H. Lieuson
Keyword(s):  
Distillation Column ◽  
Self Tuning ◽  
Distillation Column Control

Experimental Study of Fuzzy PI Controller of HEV DC/DC Converter

2013 ◽  
Vol 457-458 ◽  
pp. 1195-1199
Author(s):  
Yong Bo Zhang ◽  
Long Yun Kang
Keyword(s):  
Control Systems ◽  
Pi Controller ◽  
Motor Drive ◽  
Pi Control ◽  
Control Performance ◽  
Control Effect ◽  
Hardware Platform ◽  
Self Tuning ◽  
Fuzzy Pi Controller ◽  
Drive Circuit

This paper aims to improve the control performance of series HEV, and mainly conducts the study of the basic components of the control systems bidirectional DC/DC converter and motor drive circuit--the buck chopper and boost chopper. Also, this paper constructs a hardware platform and designs the fuzzy self-tuning PI controller. Through the comparative experiments of PI control and fuzzy PI control, it has been verified that the fuzzy PI controller has better control effect.

scholarly journals Self-Tuning Two Degree-of-Freedom Proportional–Integral Control System Based on Reinforcement Learning for a Multiple-Input Multiple-Output Industrial Process That Suffers from Spatial Input Coupling

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 487
Author(s):  
Fumitake Fujii ◽  
Akinori Kaneishi ◽  
Takafumi Nii ◽  
Ryu’ichiro Maenishi ◽  
Soma Tanaka
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Pid Control ◽  
Multiple Input Multiple Output ◽  
Industrial Process ◽  
Pi Controller ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Self Tuning ◽  
Two Degree Of Freedom

Proportional–integral–derivative (PID) control remains the primary choice for industrial process control problems. However, owing to the increased complexity and precision requirement of current industrial processes, a conventional PID controller may provide only unsatisfactory performance, or the determination of PID gains may become quite difficult. To address these issues, studies have suggested the use of reinforcement learning in combination with PID control laws. The present study aims to extend this idea to the control of a multiple-input multiple-output (MIMO) process that suffers from both physical coupling between inputs and a long input/output lag. We specifically target a thin film production process as an example of such a MIMO process and propose a self-tuning two-degree-of-freedom PI controller for the film thickness control problem. Theoretically, the self-tuning functionality of the proposed control system is based on the actor-critic reinforcement learning algorithm. We also propose a method to compensate for the input coupling. Numerical simulations are conducted under several likely scenarios to demonstrate the enhanced control performance relative to that of a conventional static gain PI controller.

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