scholarly journals Performance Evaluation for SE 113 Flow Control System Plant Using Self-Tuning Fuzzy PI Controller

2018 ◽  
Author(s):  
Zuriati Janin ◽  
Rosidah Sam ◽  
Marianah Masrie ◽  
Kushsairy Abdul Kadir ◽  
Noor Hazrin Hany Mohamad Hanif ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Control System ◽  
Flow Control ◽  
Pi Controller ◽  
Self Tuning ◽  
Fuzzy Pi Controller ◽  
Flow Control System

Design and Application of Self-Tuning PI Controller

2010 ◽  
Vol 43 ◽  
pp. 160-164 ◽  
Author(s):  
Xiao Hong Kong ◽  
Bao Jian Zhang ◽  
Xin Hua Mao ◽  
Yan Feng Chen ◽  
Chang Yuan Song
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Permanent Magnet Synchronous Motor ◽  
Pi Controller ◽  
Compound Control ◽  
Self Tuning ◽  
Fuzzy Pi Controller ◽  
Overall Performance ◽  
Application Fields

The permanent magnet synchronous motor (PMSM) is popularly used in many application fields for such advantages as having the speed-torque characteristics similar to that of a DC motor. Nevertheless, the overall performance of the PMSM is largely dependent on that of the control system. The classical PID controller, which has acquired wide applications in many fields, is only suitable for the design of the linear system and cannot meet the requirements of the nonlinear system like the PMSM. In this paper, a compound control system combining the classical PID control and the fuzzy control is presented to meet the requirements of control system. Simulation results show that the fuzzy PI controller has better performance than that of the classical PI controller.

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.

Object-Oriented Modeling of Intake Air Flow Control System

2001 ◽  
Author(s):  
T. Carlsson ◽  
A. Sokolov ◽  
A. Idebrant ◽  
M. Jirstrand
Keyword(s):  
Control System ◽  
Flow Control ◽  
Air Flow ◽  
Object Oriented ◽  
Flow Control System ◽  
Air Flow Control
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