Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System

The pneumatic actuator is widely used in the automation industry and the field of automatic control, especially in positioning control, is highly in demand. However, the pneumatic actuator has difficulties to control due to the nonlinear factors such as air compressibility and friction. Therefore, t...

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Main Author:	Abd Rahman, Azira
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/25452/1/Predictive%20Functional%20Control%20With%20Reduced-Order%20Observer%20Design%20Using%20Particle%20Swarm%20Optimization%20For%20Pneumatic%20System.pdf http://eprints.utem.edu.my/id/eprint/25452/2/Predictive%20Functional%20Control%20With%20Reduced-Order%20Observer%20Design%20Using%20Particle%20Swarm%20Optimization%20For%20Pneumatic%20System.pdf
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id	my-utem-ep.25452
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Osman, Khairuddin
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Abd Rahman, Azira Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
description	The pneumatic actuator is widely used in the automation industry and the field of automatic control, especially in positioning control, is highly in demand. However, the pneumatic actuator has difficulties to control due to the nonlinear factors such as air compressibility and friction. Therefore, this research will design a controller that focused on positioning control of Intelligent Pneumatic Actuator (IPA). This research aimed to develop a Predictive Functional Control using Reduced-Order Observer (PFC-ROO) to reduce the complexity of the pneumatic system. An optimization technique will be implemented in this project using Particle Swarm Optimization (PSO) algorithm. PSO is used to tuning the value of parameter time constant in Predictive Functional Control (PFC) to solve the problem for manual tuning. PSO will identify the best value of the parameter time constant associated with PFC for both PFC-ROO and PFC-FOO. Development of PFC-ROO algorithm is considered as a new control strategy for Intelligent Pneumatic Actuator (IPA) system for position control. This research is used the MATLAB/Simulink as a platform. The simulation results for both controllers will then be evaluated and validated using Data Acquisition (DAQ) card with a real-time experiment. In the real-time experiment, the horizontal position will be tested with different loads. Then, the result has been compared and validated the performance based on transient response analysis with existing controller Predictive Functional Control with Full-order Observer (PFC-FOO). The development will be analyzed in terms of smaller steady-state error (ess), 0 overshoot (%OS), faster settling time (Ts) and rise time (Tr) in simulation and real-time experiment. The result shows that the new development of PFC-ROO with optimization technique offers better performance compared to existing controller PFC-FOO with PSO. The best result for PFC-ROO, ess is 0.11 mm, %OS is 0%, Ts is 0.9247 seconds and Tr is 0.6620 seconds when time constant equal to 0.9023 where the best result for PFC-FOO when time constant equal to 0.9062 the ess is 0.25 mm, OS is 0%, Ts is 0.9192 seconds and Tr is 0.6638 seconds. The results revealed that the new method can reduce error by up to 56% of steady-state error.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abd Rahman, Azira
author_facet	Abd Rahman, Azira
author_sort	Abd Rahman, Azira
title	Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
title_short	Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
title_full	Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
title_fullStr	Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
title_full_unstemmed	Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System
title_sort	predictive functional control with reduced-order observer design using particle swarm optimization for pneumatic system
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronics and Computer Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25452/1/Predictive%20Functional%20Control%20With%20Reduced-Order%20Observer%20Design%20Using%20Particle%20Swarm%20Optimization%20For%20Pneumatic%20System.pdf http://eprints.utem.edu.my/id/eprint/25452/2/Predictive%20Functional%20Control%20With%20Reduced-Order%20Observer%20Design%20Using%20Particle%20Swarm%20Optimization%20For%20Pneumatic%20System.pdf
_version_	1747834131823198208
spelling	my-utem-ep.254522021-12-12T22:33:34Z Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System 2020 Abd Rahman, Azira T Technology (General) TJ Mechanical engineering and machinery The pneumatic actuator is widely used in the automation industry and the field of automatic control, especially in positioning control, is highly in demand. However, the pneumatic actuator has difficulties to control due to the nonlinear factors such as air compressibility and friction. Therefore, this research will design a controller that focused on positioning control of Intelligent Pneumatic Actuator (IPA). This research aimed to develop a Predictive Functional Control using Reduced-Order Observer (PFC-ROO) to reduce the complexity of the pneumatic system. An optimization technique will be implemented in this project using Particle Swarm Optimization (PSO) algorithm. PSO is used to tuning the value of parameter time constant in Predictive Functional Control (PFC) to solve the problem for manual tuning. PSO will identify the best value of the parameter time constant associated with PFC for both PFC-ROO and PFC-FOO. Development of PFC-ROO algorithm is considered as a new control strategy for Intelligent Pneumatic Actuator (IPA) system for position control. This research is used the MATLAB/Simulink as a platform. The simulation results for both controllers will then be evaluated and validated using Data Acquisition (DAQ) card with a real-time experiment. In the real-time experiment, the horizontal position will be tested with different loads. Then, the result has been compared and validated the performance based on transient response analysis with existing controller Predictive Functional Control with Full-order Observer (PFC-FOO). The development will be analyzed in terms of smaller steady-state error (ess), 0 overshoot (%OS), faster settling time (Ts) and rise time (Tr) in simulation and real-time experiment. The result shows that the new development of PFC-ROO with optimization technique offers better performance compared to existing controller PFC-FOO with PSO. The best result for PFC-ROO, ess is 0.11 mm, %OS is 0%, Ts is 0.9247 seconds and Tr is 0.6620 seconds when time constant equal to 0.9023 where the best result for PFC-FOO when time constant equal to 0.9062 the ess is 0.25 mm, OS is 0%, Ts is 0.9192 seconds and Tr is 0.6638 seconds. The results revealed that the new method can reduce error by up to 56% of steady-state error. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25452/ http://eprints.utem.edu.my/id/eprint/25452/1/Predictive%20Functional%20Control%20With%20Reduced-Order%20Observer%20Design%20Using%20Particle%20Swarm%20Optimization%20For%20Pneumatic%20System.pdf text en public http://eprints.utem.edu.my/id/eprint/25452/2/Predictive%20Functional%20Control%20With%20Reduced-Order%20Observer%20Design%20Using%20Particle%20Swarm%20Optimization%20For%20Pneumatic%20System.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119758 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Engineering Osman, Khairuddin 1. Adam, A., Faiz, A., Abidin, Z., Ibrahim, Z., Husain, A. R., Yusof, Z. and Ibrahim, I., 2010. A Particle Swarm Optimization Approach to Robotic Drill Route Optimization. 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Predictive Functional Control With Reduced-Order Observer Design Using Particle Swarm Optimization For Pneumatic System

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