Implementation of Fuzzy-PD for Folding Machine Prototype Using LEGO EV3

Int. J. Art. Intell. Research

Zuliari

Firmansyah

et al. 2019

The arm manipulator application that has 1 degree of freedom (DOF) can be used to flip folders in folding machines. The used actuator in folding machine is a Brushed DC motor. The Fuzzy-PD control system in this application is using Arduino Mega2560 microcontroller. Application of Fuzzy-PD control system on folding machine is able to move 5 flip folders. This Fuzzy-PD control system is able to improve the system response when there is a load, where the difference is only 7.73% from the no load condition. In addition, to implement this flip folder, a stochastic based method (ARX) is also applied on this system so it can be simulated directly. The simulation results from this folding machine model have a time constant of 0.510s, rise time of 1.501s, settling time of 1.5320s and delay time of 0.353s.

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An Implementation of Fuzzy PD Control Design for Five Flip Folders Folding Machine Using Arduino Mega2560

Int. J. Art. Intell. Research

Zuliari

Firmansyah

et al. 2019

“…The basic difference between Sugeno and Mamdani fuzzy lies only in the defuzzification process. Mamdani defuzzification searched by finding the centre of gravity on the membership form area, while Sugeno defuzzification is determined by looking for a relatively simple average weight value [7]- [11] with (1).…”

Section: Methods 21 Zero Order Fuzzy Sugeno Designmentioning

confidence: 99%

The application of Sugeno fuzzy to control active power load and remaining battery usage time modelling

Firmansyah

Issany

et al. 2022

IJEECS

This study aimed to propose a control scheme to <span>optimize the active power load using the pulse-width adjustment technique and the MOSFET driver. The power used and stored in the battery is an input for the fuzzy system whose information is obtained through the current (ACS 712) and voltage sensor readings. The use of fuzzy to control the power of two 5-watt lamps is more efficient than the manual technique using an ordinary switch. This is because fuzzy only consumes 3.25 Watt/hour while the manual technique requires 5.7 Watt/hour. Based on the linear regression-based estimation, the fuzzy technique lasts ±17 hours from the initial power of 55.82 Wh, or 5.5 hours longer than the manual technique that lasts only ±11.5 hours from the initial power of 65.62 Wh. Therefore, this study adjusted the load power to extend battery life and increase solar energy use efficiency and innovations in load control based on available resources.</span>

“…This system is the development of the use of manual folding equipment, which was the initial idea for making this tool. In the following research, a prototype folding machine appeared using the LEGO Mindstorm EV3 robot module [7]. The use of this robot module aims to simplify design because all devices are easily compiled.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Flip Folder Folding Machine Using Artificial Neuro Network Method with NARX (Nonlinear Auto Regressive Exogenous) Structure

Prabowo

Imaduddin³

2020

Inform

Folding machine is a tool that is needed in the small and medium scale laundry industry that has a goal for the efficiency of production time. The flip folder is the main component of this tool, which functions to fold the clothes by moving to form a certain deflection angle where the movement process is controlled by the controller. The system modeling process is the first step to study the characteristics of the system. In a dynamic system, the form of linear modeling is approved difficult to obtain a model that represents the actual physical model. Selecting the structure of the NARX (Nonlinear Autoregressive eXogenous) model was chosen to obtain the dynamic nature of the system. An estimation method to obtain parameter values from the system used Artificial Neural Networks (ANN), which is a trading scheme to be able to predict the output of a system that uses input data and output. Based on the offline assessment process using measurement data obtained by the NARX ANN model on the variation of the number of layers in 30 with a value of MSE 0,38641.