LQR controller tuning by using particle swarm optimization

LQR is an optimal controller. Optimal in that it is defined so as to provide the smallest possible error to its input. Q and R matrix of LQR usually selected by trial and error. In two wheeled inverted pendulum robot, the most important variable to control is tilt angle. Therefore in this thesis, th...

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Main Author: M. Lamin Gabasa, Hagag
Format: Thesis
Language:English
Published: 2009
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Online Access:http://eprints.utm.my/id/eprint/12197/6/HagagMLaminMFKE2009.pdf
http://eprints.utm.my/id/eprint/12197/
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spelling my.utm.121972017-09-17T06:56:21Z http://eprints.utm.my/id/eprint/12197/ LQR controller tuning by using particle swarm optimization M. Lamin Gabasa, Hagag TK Electrical engineering. Electronics Nuclear engineering LQR is an optimal controller. Optimal in that it is defined so as to provide the smallest possible error to its input. Q and R matrix of LQR usually selected by trial and error. In two wheeled inverted pendulum robot, the most important variable to control is tilt angle. Therefore in this thesis, the value of Q is firstly set and then R the identity matrix is set. For small rising time and low overshoot for the overall control. After getting good value of Q, the feedback gain K is obtained. By using MATLAB simulink, we simulated new PSO algorithm for the LQR control to select the best Q control matrix. The selection is based on the smallest integral of absolute error of the random Q. From the simulation results, the very challenging controller design for the TWIP control system has been realized by the PSO-based LQ regulator. It is our firm belief that the proposed method is use useful not only for the control of TWIP robot problem but also for other difficult problems. 2009-11 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/12197/6/HagagMLaminMFKE2009.pdf M. Lamin Gabasa, Hagag (2009) LQR controller tuning by using particle swarm optimization. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
M. Lamin Gabasa, Hagag
LQR controller tuning by using particle swarm optimization
description LQR is an optimal controller. Optimal in that it is defined so as to provide the smallest possible error to its input. Q and R matrix of LQR usually selected by trial and error. In two wheeled inverted pendulum robot, the most important variable to control is tilt angle. Therefore in this thesis, the value of Q is firstly set and then R the identity matrix is set. For small rising time and low overshoot for the overall control. After getting good value of Q, the feedback gain K is obtained. By using MATLAB simulink, we simulated new PSO algorithm for the LQR control to select the best Q control matrix. The selection is based on the smallest integral of absolute error of the random Q. From the simulation results, the very challenging controller design for the TWIP control system has been realized by the PSO-based LQ regulator. It is our firm belief that the proposed method is use useful not only for the control of TWIP robot problem but also for other difficult problems.
format Thesis
author M. Lamin Gabasa, Hagag
author_facet M. Lamin Gabasa, Hagag
author_sort M. Lamin Gabasa, Hagag
title LQR controller tuning by using particle swarm optimization
title_short LQR controller tuning by using particle swarm optimization
title_full LQR controller tuning by using particle swarm optimization
title_fullStr LQR controller tuning by using particle swarm optimization
title_full_unstemmed LQR controller tuning by using particle swarm optimization
title_sort lqr controller tuning by using particle swarm optimization
publishDate 2009
url http://eprints.utm.my/id/eprint/12197/6/HagagMLaminMFKE2009.pdf
http://eprints.utm.my/id/eprint/12197/
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score 13.160551