scholarly journals Application of the Fuzzy Method in the Design of Control and Monitoring Systems for Flood Canal Pump Houses

CCIT Journal ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 203-213
Author(s):  
Lukman Medriavin Silalahi ◽  
Linggar Amnesta Virgian

The purpose of making a prototype of the canal to flood the pump house is namely as a means of ideas that may later be applied to the pump house in Indonesia. At the end of this task will be made protorype flood canal house pump with which it can work according to the speed of the incoming flow and the height of the water in the canal, on hulu there are 1 pump 12V which will drain the water into the canal and also waterflow as a tool that can detect the incoming flow. At the pump house there are 3 pump and 1 water level which will work according to the inflow and the height of the channel. Fuzzy Logic is one method of system control can provide decision that resembles the human decision. In the design process of this plant, used a system of development control of the fuzzy logic system using Arduino. It is intended for a design on the plant control system and monitoring of the pump house flood canal. The use of the Speed Controller PWM on the circuit can work well in regulating the pump speed in the work. Pump 1 and pump 2 has a maximum value of the PWM 144 that can remove water 180 ml that works on low currents and are and sodetan have the value of the PWM to 255 that can get water out of 318 ml of working on a heavy flow.

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa

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.


2016 ◽  
Vol 12 (2) ◽  
pp. 188-197
Author(s):  
A yahoo.com ◽  
Aumalhuda Gani Abood aumalhuda ◽  
A comp ◽  
Dr. Mohammed A. Jodha ◽  
Dr. Majid A. Alwan

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis


2013 ◽  
Vol 37 (3) ◽  
pp. 611-620
Author(s):  
Ing-Jr Ding ◽  
Chih-Ta Yen

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.


2019 ◽  
Vol 52 (9-10) ◽  
pp. 1344-1353 ◽  
Author(s):  
Gang Chen ◽  
Weigong Zhang ◽  
Xu Li ◽  
Bing Yu

To solve the shortcomings of existing control methods for an electromagnetic direct drive vehicle robot driver, including large speed tracking error and large mileage deviation, a new adaptive speed control method for the electromagnetic direct drive vehicle robot driver based on fuzzy logic is proposed in this paper. The electromagnetic direct drive vehicle robot driver adapts an electromagnetic linear motor as its drive mechanism. The control system structure is designed. The coordinated controller for multiple manipulators is presented. Moreover, an adaptive speed controller for the electromagnetic direct drive vehicle robot driver is proposed to achieve the accurate tracking of desired speed. Experiments are conducted using a Ford FOCUS car. Performances of the proposed method, proportional–integral–derivative, and fuzzy neural network are compared and analyzed. Experimental results demonstrate that the proposed control method can accurately track the target speed, and it can inhabit the change of speed caused by interference under different test conditions, and it has small mileage deviation, which can meet the requirements of national vehicle test standards.


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