PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode
In this paper, the state feedback control design problem for stabilizing a two-wheeled wheelchair in balancing mode is considered. The system is modeled as double-inverted pendulum on two wheels. The calculation of state feedback control gains is conventionally handled by LQR method via Riccati equ...
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iium-451702017-09-18T09:37:58Z http://irep.iium.edu.my/45170/ PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode Aula, Abqori Ahmad, Salmiah Akmeliawati, Rini TJ212 Control engineering In this paper, the state feedback control design problem for stabilizing a two-wheeled wheelchair in balancing mode is considered. The system is modeled as double-inverted pendulum on two wheels. The calculation of state feedback control gains is conventionally handled by LQR method via Riccati equation. Unfortunately, the method still possesses trial and error approach when choosing some parameters, in particular tuning the elements of Q and R weighting matrices. Therefore, an intelligent numerical method to resolve this problem is proposed by adopting PSO algorithm. The simulation work is carried out to evaluate the effectiveness of the proposed method. The result shows that the numerical method reduces tuning time and improves the performance of the system. IEEE 2015 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/45170/1/45170.pdf application/pdf en http://irep.iium.edu.my/45170/4/45170_PSO-based%20state%20feedback%20regulator_scopus.pdf Aula, Abqori and Ahmad, Salmiah and Akmeliawati, Rini (2015) PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode. In: 2015 10th Asian Control Conference (ASCC 2015), 31st May- 3rd June 2015, Kota Kinabalu, Sabah. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7244680 10.1109/ASCC.2015.7244680 |
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TJ212 Control engineering |
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TJ212 Control engineering Aula, Abqori Ahmad, Salmiah Akmeliawati, Rini PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
description |
In this paper, the state feedback control design
problem for stabilizing a two-wheeled wheelchair in balancing mode is considered. The system is modeled as double-inverted pendulum on two wheels. The calculation of state feedback control gains is conventionally handled by LQR method via Riccati equation. Unfortunately, the method still possesses trial and error approach when choosing some parameters, in particular tuning the elements of Q and R weighting matrices. Therefore, an intelligent numerical method to resolve this problem is proposed by adopting PSO algorithm. The simulation
work is carried out to evaluate the effectiveness of the proposed method. The result shows that the numerical method reduces tuning time and improves the performance of the system. |
format |
Conference or Workshop Item |
author |
Aula, Abqori Ahmad, Salmiah Akmeliawati, Rini |
author_facet |
Aula, Abqori Ahmad, Salmiah Akmeliawati, Rini |
author_sort |
Aula, Abqori |
title |
PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
title_short |
PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
title_full |
PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
title_fullStr |
PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
title_full_unstemmed |
PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
title_sort |
pso-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode |
publisher |
IEEE |
publishDate |
2015 |
url |
http://irep.iium.edu.my/45170/ http://irep.iium.edu.my/45170/ http://irep.iium.edu.my/45170/ http://irep.iium.edu.my/45170/1/45170.pdf http://irep.iium.edu.my/45170/4/45170_PSO-based%20state%20feedback%20regulator_scopus.pdf |
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2023-09-18T21:04:18Z |
last_indexed |
2023-09-18T21:04:18Z |
_version_ |
1777410829844480000 |