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Implementation of Fuzzy-PD for Folding Machine Prototype Using LEGO EV3 Wahyu S. Pambudi; Titiek Suheta
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.7569

Folding machine prototype is a tool for folding clothes that used a LEGO Mindstorm EV3 and the movement of this LEGO will be controlled by using the proposed method.This research is using 2 kind of systems, first is (Proportional Integration Derrivative) and second is Fuzzy-PD. This system can improve the production efficiency especially in micro convection-based industry (UKM). This folding machine prototype has 3 folds connected to the EV3 Large Motors as actuators. PID and Fuzzy-PD control systems are used to control the position of the motor angle by reading the rotary encoder. Based on the test results with load condition at Kp=7,00; Ki=2.00; Kd=15.5,the PID control system has a rise time of 0.479s with settling time of 0.551s and overshoot value of 9,1%. While the result of Fuzzy-PD control shows the rise time is equal to 0.617s with settling time of 0.7s and overshoot value of 7.5%.

Simulation design of trajectory planning robot manipulator Wahyu S. Pambudi; Enggar Alfianto; Andy Rachman; Dian Puspita Hapsari
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.

Simulation design of trajectory planning robot manipulator Wahyu S. Pambudi; Enggar Alfianto; Andy Rachman; Dian Puspita Hapsari
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.

Simulation design of trajectory planning robot manipulator Wahyu S. Pambudi; Enggar Alfianto; Andy Rachman; Dian Puspita Hapsari
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.

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