Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artif...

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Main Authors:	Nur Iffah, Mohamed Azmi, Kamal Arifin, Mat Piah, Wan Azhar, Wan Yusoff, F. R. M., Romlay
Format:	Conference or Workshop Item
Language:	English English
Published:	2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/19722/ http://umpir.ump.edu.my/id/eprint/19722/1/fkp-2017-iffah-Optimization%20of%20PID%20Parameters%20Utilizing%20Variable1.pdf http://umpir.ump.edu.my/id/eprint/19722/2/fkp-2017-iffah-Optimization%20of%20PID%20Parameters%20Utilizing%20Variable.pdf

id	ump-19722
recordtype	eprints
spelling	ump-197222018-09-26T08:08:13Z http://umpir.ump.edu.my/id/eprint/19722/ Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization Nur Iffah, Mohamed Azmi Kamal Arifin, Mat Piah Wan Azhar, Wan Yusoff F. R. M., Romlay TK Electrical engineering. Electronics Nuclear engineering Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey-Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system. 2017 Conference or Workshop Item NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19722/1/fkp-2017-iffah-Optimization%20of%20PID%20Parameters%20Utilizing%20Variable1.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/19722/2/fkp-2017-iffah-Optimization%20of%20PID%20Parameters%20Utilizing%20Variable.pdf Nur Iffah, Mohamed Azmi and Kamal Arifin, Mat Piah and Wan Azhar, Wan Yusoff and F. R. M., Romlay (2017) Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization. In: The 4th Asia Pacific Conference on Manufacturing Systems and The 3rd International Manufacturing Engineering Conference (APCOMS-IMEC 2017), 7-8 December 2017 , Yogyakarta, Indonesia. pp. 1-6.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Nur Iffah, Mohamed Azmi Kamal Arifin, Mat Piah Wan Azhar, Wan Yusoff F. R. M., Romlay Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
description	Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey-Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.
format	Conference or Workshop Item
author	Nur Iffah, Mohamed Azmi Kamal Arifin, Mat Piah Wan Azhar, Wan Yusoff F. R. M., Romlay
author_facet	Nur Iffah, Mohamed Azmi Kamal Arifin, Mat Piah Wan Azhar, Wan Yusoff F. R. M., Romlay
author_sort	Nur Iffah, Mohamed Azmi
title	Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
title_short	Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
title_full	Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
title_fullStr	Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
title_full_unstemmed	Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
title_sort	optimization of pid parameters utilizing variable weight grey-taguchi method and particle swarm optimization
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/19722/ http://umpir.ump.edu.my/id/eprint/19722/1/fkp-2017-iffah-Optimization%20of%20PID%20Parameters%20Utilizing%20Variable1.pdf http://umpir.ump.edu.my/id/eprint/19722/2/fkp-2017-iffah-Optimization%20of%20PID%20Parameters%20Utilizing%20Variable.pdf
first_indexed	2023-09-18T22:28:15Z
last_indexed	2023-09-18T22:28:15Z
_version_	1777416112259989504

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

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