A PI type self-tuning fuzzy controller with receding horizon optimization

2005 ◽  
Author(s):  
Min Xu ◽  
Shaoyuan Li
Keyword(s):  
Fuzzy Controller ◽  
Receding Horizon ◽  
Self Tuning

Single Degree Vibration Platform Self-Tuning Fuzzy PID Controller Design Based on AMESim

2013 ◽  
Vol 310 ◽  
pp. 518-523
Author(s):  
Zhi Qiang Chao ◽  
Xin Ze Li ◽  
Ai Hong Meng
Keyword(s):  
Hydraulic System ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Simulation Software ◽  
Fuzzy Pid ◽  
Hydraulic Simulation ◽  
Fuzzy Pid Controller ◽  
Single Degree ◽  
Self Tuning

In recent years, hydraulic simulation has become an important means to research hydraulic system, in order to enable the single degree platform vibration curve with better traceability and reach the requirement of the test, this paper represent single degree system platform stimulated by simulation software AMESim, taking the Single degree freedom vibration hydraulic system as an example, MATlab/simulink is applied to the design of the vibration platform system fuzzy PID controller. Through the comparison between the simulation test and traditional PID controller, the designed self-tuning fuzzy controller can control the platform better, with smaller overshoot, faster response, shorter adjusting time, as well as fulfill the permissible accuracy.

A study on water level control of PWR steam generator at low power and the self-tuning of its fuzzy controller

1995 ◽  
Vol 74 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Nanju Na ◽  
Keechoon Kwon ◽  
Changshik Ham ◽  
Zeungnam Bien
Keyword(s):  
Low Power ◽  
Water Level ◽  
Steam Generator ◽  
Fuzzy Controller ◽  
The Self ◽  
Level Control ◽  
Self Tuning

Self Tuning Based Adaptive Fuzzy Logic Controller in Lab view for Sterilizing Equipments

2014 ◽  
Vol 3 (2) ◽  
pp. 84
Author(s):  
P. J. Ragu
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Lab View ◽  
Adaptive Fuzzy Logic ◽  
Self Tuning ◽  
Input Variables ◽  
Temperature Errors

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning  Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

scholarly journals Self-Tuning Fuzzy Controller

1988 ◽  
Vol 24 (2) ◽  
pp. 191-197 ◽  
Author(s):  
Mikio MAEDA ◽  
Shuta MURAKAMI
Keyword(s):  
Fuzzy Controller ◽  
Self Tuning

Positioning System with Progressive Wave-Type Ultrasonic Motor under Self-Tuning Fuzzy Control

1995 ◽  
Vol 7 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Junji Fukumi ◽  
◽  
Takuya Kamano ◽  
Takayuki Suzuki ◽  
Yu Kataoka ◽  
...  
Keyword(s):  
Feedback Loop ◽  
Fuzzy Controller ◽  
Lower Layer ◽  
Tracking Error ◽  
Ultrasonic Motor ◽  
The Self ◽  
Positioning System ◽  
Wave Type ◽  
Nonlinear Characteristics ◽  
Self Tuning

This paper considers the use of a self-tuning fuzzy controller for a positioning system with a progressive wavetype ultrasonic motor. The system consists of a feedback loop with a conventional controller and a self tuning fuzzy controller. The objective of the self tuning fuzzy controller is to restrain the adverse effect of nonlinear characteristics of the motor and to improve the tracking performance. The self-tuning fuzzy controller is functionally divided into two layers. The fuzzy rules are automatically adjusted by a tuning algorithm so that the tracking error is minimized in the upper layer. In lower layer, the output signal of the self tuning fuzzy controller is obtained by fuzzy reasoning procedure. After the tuning process is completed, the tracking error almost converges to zero, and the ultrasonic motor is no longer controlled by the fixed gain feedback controller but by the self-tuning fuzzy controller. The effectiveness of the proposed self-tuning fuzzy controller is demonstrated by an experiment.

Optimal self-tuning fuzzy controller

1995 ◽  
Author(s):  
Syed A. Akbar ◽  
Ramon Parra-Loera
Keyword(s):  
Fuzzy Controller ◽  
Self Tuning
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