NDLI: Proportional-integrative controller design of Pneumatic system using particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Osman, K. Mohd Faudzi, A.A. Rahmat, M.F. Mustafa, N.D. Abidin, A.F.Z. Suzumori, K.
Copyright Year	2013
Description	Author affiliation: Centre for Artificial Intell. & Robot., Univ. Teknol. Malaysia, Skudai, Malaysia (Mohd Faudzi, A.A.) \|\| Dept. of Ind. Electron., Univ. Teknikal Malaysia Melaka, Durian Tunggal, Malaysia (Osman, K.) \|\| Grad. Sch. of Natural Sci. & Technol., Okayama Univ., Okayama, Japan (Suzumori, K.) \|\| Dept. of Control & Mechatron. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia (Rahmat, M.F.; Mustafa, N.D.; Abidin, A.F.Z.)
Abstract	Pneumatic systems are well known for their advantages, simplicity and can be applied in various industrial applications. This paper presents a comparative study of tuning methods on Proportional-Integral (PI) controller for position control performance both in simulation and in real system. A PI controller designed by optimization with the Particle Swarm Optimization (PSO) algorithm is proposed and its output is compared with the Ziegler-Nichols (Z-N) formula and manual (trial and error) results. Performance assessment of the tuned controllers was performed in MATLAB Simulink. It was observed that the controller designed using PSO gives better results and performance compared to the Z-N formula and manual tuning in simulation and real-time.
Starting Page	421
Ending Page	426
File Size	326416
Page Count	6
File Format	PDF
ISBN	9781479926565
DOI	10.1109/SCOReD.2013.7002623
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mechatronics Pneumatic Proportional-Integral (PI) Particle Swarm Optimization (PSO) Pneumatic systems Real-time systems System identification Mathematical model System Identification (SI) Particle swarm optimization Tuning
Content Type	Text
Resource Type	Article

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