Identification and cascade control of servo-pneumatic system using Particle Swarm Optimization

Ashraf Saleem, Bashar Taha, Tarek Tutunji, Ahmad Al-Qaisia

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

This paper presents a cascade control methodology for pneumatic systems using Particle Swarm Optimization (PSO). First, experimental data is collected and used to identify the servo-pneumatic system where an Auto-Regressive Moving-Average (ARMA) model is formulated using PSO algorithm. Then, cascaded Proportional-Integral-Derivative (PID) controller with PSO tuning is proposed and implemented on real system using Hardware-In-the-Loop (HIL). The identified model is validated experimentally and the performance of the cascaded-PID controller is tested under various conditions of speed variation. Experimental results show that cascaded-PID with PSO tuning performs better than single-PID, especially in disturbance rejection (a practical challenge in industrial pneumatic systems). Results also show that cascaded-PID with PSO-tuning performs better than cascaded-PID with self-tuning in the transient and steady-state responses.

Original languageEnglish
Pages (from-to)164-179
Number of pages16
JournalSimulation Modelling Practice and Theory
Volume52
DOIs
Publication statusPublished - 2015

Fingerprint

Cascade Control
Servo System
Cascades (fluid mechanics)
Pneumatics
Particle swarm optimization (PSO)
Particle Swarm Optimization
Directly proportional
Derivatives
Derivative
Tuning
Controller
Hardware-in-the-loop
Autoregressive Moving Average Model
Controllers
Self-tuning
Transient State
Disturbance Rejection
Disturbance rejection
Particle Swarm Optimization Algorithm
Experimental Data

Keywords

  • Cascaded control
  • Optimization
  • Particle
  • PID control
  • Servo-pneumatic system
  • Swarm
  • System identification

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Hardware and Architecture

Cite this

Identification and cascade control of servo-pneumatic system using Particle Swarm Optimization. / Saleem, Ashraf; Taha, Bashar; Tutunji, Tarek; Al-Qaisia, Ahmad.

In: Simulation Modelling Practice and Theory, Vol. 52, 2015, p. 164-179.

Research output: Contribution to journalArticle

@article{08b79ae8fd1647508d90b3173f489d9a,
title = "Identification and cascade control of servo-pneumatic system using Particle Swarm Optimization",
abstract = "This paper presents a cascade control methodology for pneumatic systems using Particle Swarm Optimization (PSO). First, experimental data is collected and used to identify the servo-pneumatic system where an Auto-Regressive Moving-Average (ARMA) model is formulated using PSO algorithm. Then, cascaded Proportional-Integral-Derivative (PID) controller with PSO tuning is proposed and implemented on real system using Hardware-In-the-Loop (HIL). The identified model is validated experimentally and the performance of the cascaded-PID controller is tested under various conditions of speed variation. Experimental results show that cascaded-PID with PSO tuning performs better than single-PID, especially in disturbance rejection (a practical challenge in industrial pneumatic systems). Results also show that cascaded-PID with PSO-tuning performs better than cascaded-PID with self-tuning in the transient and steady-state responses.",
keywords = "Cascaded control, Optimization, Particle, PID control, Servo-pneumatic system, Swarm, System identification",
author = "Ashraf Saleem and Bashar Taha and Tarek Tutunji and Ahmad Al-Qaisia",
year = "2015",
doi = "10.1016/j.simpat.2015.01.007",
language = "English",
volume = "52",
pages = "164--179",
journal = "Simulation Modelling Practice and Theory",
issn = "1569-190X",
publisher = "Elsevier",

}

TY - JOUR

T1 - Identification and cascade control of servo-pneumatic system using Particle Swarm Optimization

AU - Saleem, Ashraf

AU - Taha, Bashar

AU - Tutunji, Tarek

AU - Al-Qaisia, Ahmad

PY - 2015

Y1 - 2015

N2 - This paper presents a cascade control methodology for pneumatic systems using Particle Swarm Optimization (PSO). First, experimental data is collected and used to identify the servo-pneumatic system where an Auto-Regressive Moving-Average (ARMA) model is formulated using PSO algorithm. Then, cascaded Proportional-Integral-Derivative (PID) controller with PSO tuning is proposed and implemented on real system using Hardware-In-the-Loop (HIL). The identified model is validated experimentally and the performance of the cascaded-PID controller is tested under various conditions of speed variation. Experimental results show that cascaded-PID with PSO tuning performs better than single-PID, especially in disturbance rejection (a practical challenge in industrial pneumatic systems). Results also show that cascaded-PID with PSO-tuning performs better than cascaded-PID with self-tuning in the transient and steady-state responses.

AB - This paper presents a cascade control methodology for pneumatic systems using Particle Swarm Optimization (PSO). First, experimental data is collected and used to identify the servo-pneumatic system where an Auto-Regressive Moving-Average (ARMA) model is formulated using PSO algorithm. Then, cascaded Proportional-Integral-Derivative (PID) controller with PSO tuning is proposed and implemented on real system using Hardware-In-the-Loop (HIL). The identified model is validated experimentally and the performance of the cascaded-PID controller is tested under various conditions of speed variation. Experimental results show that cascaded-PID with PSO tuning performs better than single-PID, especially in disturbance rejection (a practical challenge in industrial pneumatic systems). Results also show that cascaded-PID with PSO-tuning performs better than cascaded-PID with self-tuning in the transient and steady-state responses.

KW - Cascaded control

KW - Optimization

KW - Particle

KW - PID control

KW - Servo-pneumatic system

KW - Swarm

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=85027932127&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027932127&partnerID=8YFLogxK

U2 - 10.1016/j.simpat.2015.01.007

DO - 10.1016/j.simpat.2015.01.007

M3 - Article

VL - 52

SP - 164

EP - 179

JO - Simulation Modelling Practice and Theory

JF - Simulation Modelling Practice and Theory

SN - 1569-190X

ER -