VLSI Implementation of a Self-tuning Fuzzy Controller Based on Variable Universe of Discourse

2005 ◽  
pp. 1044-1052 ◽  
Author(s):  
Weiwei Shan ◽  
Dongming Jin ◽  
Weiwei Jin ◽  
Zhihao Xu
Keyword(s):  
Fuzzy Controller ◽  
Vlsi Implementation ◽  
Variable Universe ◽  
Self Tuning ◽  
Universe Of Discourse

A Variable Universe Self-Tuning Fuzzy-PI Speed Controller for Induction Motor with DTC-SVM

2015 ◽  
Vol 741 ◽  
pp. 734-738
Author(s):  
Hong Xia Yu ◽  
Zhi Cheng Chen
Keyword(s):  
Induction Motor ◽  
Fuzzy Controller ◽  
Reference Value ◽  
Response Speed ◽  
Fast Response ◽  
Torque Ripple ◽  
Variable Universe ◽  
Speed Controller ◽  
Self Tuning ◽  
Lower Torque

In the paper, To control the speed of induction motor drive with DTC-SVM, A variable universe self-tuning fuzzy-PI(VUFPI) controller was designed based on the concept of variable universe to generate the torque reference value, Two inputs of the fuzzy controller are speed error and error change rate, the output of the fuzzy controller are adjustment parameters (,) of PI controller parameters (Kp, Ki), the input and output variable universe of fuzzy controller were regulated by scale factor which is calculated according to error grade, The simulation results show that the proposed method has fast response speed and lower torque ripple.

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.

scholarly journals On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Hongwei Li ◽  
Kaide Ren ◽  
Haiying Dong ◽  
Shuaibing Li
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Fuzzy Controller ◽  
Synchronous Generator ◽  
Variable Universe ◽  
Neural Network Controller ◽  
Front End ◽  
Network Controller ◽  
Drive Chain

The rapid development of wind generation technology has boosted types of the new topology wind turbines. Among the recently invented new wind turbines, the front-end speed regulated (FSR) wind turbine has attracted a lot of attention. Unlike conventional wind turbine, the speed regulation of the FSR machines is realized by adjusting the guide vane angle of a hydraulic torque converter, which is converterless and much more grid-friendly as the electrically excited synchronous generator (EESG) is also adopted. Therefore, the drive chain control of the wind turbine owns the top priority. To ensure that the FSR wind turbine performs as a general synchronous generator, this paper firstly modeled the drive chain and then proposed to use the variable-universe fuzzy approach for the drive chain control. It helps the wind generator operate in a synchronous speed and outperform other types of wind turbines. The multipopulation genetic algorithm (MPGA) is adopted to intelligently optimize the parameters of the expansion factor of the designed variable-universe fuzzy controller (VUFC). The optimized VUFC is applied to the speed control of the drive chain of the FSR wind turbine, which effectively solves the contradiction between the low precision of the fuzzy controller and the number of rules in the fuzzy control and the control accuracy. Finally, the main shaft speed of the FSR wind turbine can reach a steady-state value around 1500 rpm. The response time of the results derived using VUFC, compared with that derived from a neural network controller, is only less than 0.5 second and there is no overshoot. The case study with the real machine parameter verifies the effectiveness of the proposal and results compared with conventional neural network controller, proving its outperformance.

Adaptive Variable Universe of Discourse Fuzzy Sliding Mode Control

2014 ◽  
Vol 656 ◽  
pp. 327-334 ◽  
Author(s):  
Nasim Ullah ◽  
Faizan Ahmad Bhatti
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Poor Performance ◽  
Fuzzy Sliding Mode Control ◽  
Variable Universe ◽  
Mode Control ◽  
Adaptive Fuzzy Sliding Mode ◽  
Fuzzy Sliding Mode ◽  
Error Dynamics ◽  
Universe Of Discourse

This paper proposes adaptive variable universe of discourse fuzzy sliding mode control for efficient compensation of unbounded disturbances and reduced chattering. Classical sliding mode control is robust to bounded uncertainties and disturbances. The disadvantages of classical sliding mode control are high frequency chattering and poor performance in case of unbounded disturbances. Chattering phenomena is minimized using adaptive fuzzy sliding mode control but fuzzy fixed universe of discourse makes it in-efficient for time varying unbounded disturbances and uncertainties. This article investigates a variable universe of discourse fuzzy logic system for unbounded disturbances. Fuzzy universe of discourse for membership functions of input and output parameters is tuned online using an adaptive empirical law derived from the error dynamics. Performance of proposed control is verified using extensive simulations.

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
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