Fuzzy logic in control systems: fuzzy logic controller. II

1990 ◽  
Vol 20 (2) ◽  
pp. 419-435 ◽  
Author(s):  
C.C. Lee
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Fuzzy Logic Controller

Professor Ebrahim Mamdani Memorial Issue

2012 ◽  
Vol 16 (5) ◽  
pp. 591-591
Author(s):  
Yoshinori Arai ◽  
Toshihiko Watanabe
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Special Issue ◽  
Fuzzy Algorithms ◽  
Dynamic Plant ◽  
Memorial Issue ◽  
The Many

On February 22, 2010, Prof. Ebrahim H. Mamdani who devised Mamdani fuzzy inference has passed away. His work in fuzzy inference, which rapidly paved the way to its practical use, helped disseminate Prof. Lotfi Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1-13, 1975) with S. Assilian ? enabled fuzzy inference technology to develop dramatically both indicatively and indirectly to where it has been applied, including fuzzy control systems. This special issue honors Prof. Mamdani for his invaluable efforts in these and many other fields. We have asked for submissions by researchers influenced by Prof. Mamdanifs achievements, including his work in fuzzy inference, and have narrowed down to one review and seven full papers. The review by Hirosato Seki and Kai Meng Tay provides an incisive overview on the many aspects of fuzzy inference that Prof. Mamdani brought to light. Prof. Mamdanifs fuzzy inference has become a deterministic technology that can be chosen naturally and that will continue to be influential and survivable well into the future.

scholarly journals Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities

2021 ◽  
Vol 19 (3) ◽  
pp. 105-110
Author(s):  
A. M. Sagdatullin ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Oil And Gas ◽  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
Classical Control

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

Advanced Optimal PSO, Fuzzy and PI Controller with PMSM and WTGS at 5Hz Side of Generation and 50Hz Side of Grid

2016 ◽  
Vol 7 (1) ◽  
pp. 173
Author(s):  
Salam Waley Shneen ◽  
Chengxiong Mao ◽  
Dan Wang
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Energy Content ◽  
Pi Controller ◽  
Dc Bus

To use different control systems, Like Classical PI Controller, Expert System, Fuzzy Logic Controller and Optimization PSO Controller. It used to control for PMSM which worked in integration system to Wind Energy. Wind Energy content of wind turbine, PMSM, Rectifier, DC bus, Inverter, Filter, Load and Grid. In the first step, to run the PMSM with different speeds to get different frequency to selected the frequency in side of generation with the rated speed. Second step, solve the mathematical equation to use different values of wind speed with selected (15,20 m/s and less than with more than 15&20m/s). Third step, Calculation the power generation with wind speed (15 m/5 & 20 m/s). Fourth step, using these component system Rectifier, DC bus, Inverter, Filter, Load & Grid with WTGS & PMSM. Final step, use different control systems, Like Classical PI Controller, Expert System Fuzzy Logic Controller and Optimization PSO Controller With PMSM to analysis all results after using the simulation model of proposed variable speed based WECS. The wind turbine coupled with PMSM.A closed loop control system with a PI control,Fuzzy, PSO in the speed loop with current controllers .The simulation circuits for PMSM, inverter, speed and current controllers include all realistic components of the drive system. These results also confirmed that the transient torque and current never exceed the maximum permissible value.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

Sign in / Sign up

Export Citation Format

Share Document