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...

Full description

Saved in:

Bibliographic Details
Main Author:	M. Lamin Gabasa, Hagag
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/12197/6/HagagMLaminMFKE2009.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.12197
record_format	uketd_dc
spelling	my-utm-ep.121972017-09-17T06:56:21Z LQR controller tuning by using particle swarm optimization 2009-11 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 http://eprints.utm.my/id/eprint/12197/ http://eprints.utm.my/id/eprint/12197/6/HagagMLaminMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering 1. Abdollah M.F, Ahmad M.N, Husain A.R, Nawawi S.W and Osman J.H.S(2006). Controller design for Two-wheels Inverted Pendulum Mobile Robot Using PISMC. 4th Student Conference on Research and Development (SCORED 2006). Shah Alam, Malaysia. June 2006. pp-194-199. 2. Ahmad M.N, Nawawi S.W and Osman J.H.S(2007).Development of a Two-Wheeled Inverted Pendelum Mobile Robot. 5th Student Conference on Research and Development (SCORED 2007). Malaysia. December 2007. 3. Ker-Wei, Yu, Zhi-Liang, Huang, LQ Regulator Design Based on Particle Swarm Optimization Ker-Wei, Yu, Member, IEEE, Zhi-Liang, Huang. 2006 IEEE Conference on Systems, Man, and Cybernetics October 8-11, 2006, Taipei, Taiwan 4. J. Kennedy, and R. Eberhart, “Particle swarm optimization,” Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948. 1995. 5. Kim, Y.H., Kim, S.H., Kwak, Y.K. ”Dynamic Analysis of a Nonholonomic Two-wheeled Inverted Pendulum Robot”, Proc. of the Eighth Int. Symp. on Artificial Life and Robotics(AROB8th, '03), P.415418, Beppu, Oita, Japan, 24-26 January, 2003. 6. Jixin Qian, Liyan Zhang, Longhua Ma and Yongling Zheng(2003). Robust PID controller design using Particle Swarm Optimizer. Proceeding of the 2003 International Symposium on Intelligent Control. Housan, texas. October 2003. pp-974-979 7. R. C. Eberhart, and Y. Shi, “Particle swarm optimization: developments, applications and resources,” Proceedings of the IEEE Congress on Evolutionary Computation, Seoul, Korea, 2001. 8. Y. H. Lin, Double Link Inverse Pendulum System Swing up and Balance Control. Master Degree Dissertation, National Cheng Kung University, Taiwan, 2003. 9. J. Vondrich, E. Thondel, Modelling of LQR Control with Matlab, 2009 10. Arango, E. Calvente, J. Giral, R. El Aroudi, A. Martinez-Salamero, L. , LQR control of an asymmetrical interleaved boost converter working in inherent DCM, Industrial Electronics, 2005. ISIE 2005. Proceedings of the IEEE International Symposium on, Publication Date: 20-23 June 2005
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
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
qualification_level	Master's degree
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
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/12197/6/HagagMLaminMFKE2009.pdf
_version_	1747814905679970304

LQR controller tuning by using particle swarm optimization

Similar Items