T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum

This article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base...

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Main Authors: Boutaina Elkinany, Mohammed Alfidi, Redouane Chaibi, Zakaria Chalh
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Advances in Fuzzy Systems
Online Access:http://dx.doi.org/10.1155/2020/8835511
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spelling doaj-af4f2c6b35fb472587285d7da2c3d2632020-12-21T11:41:28ZengHindawi LimitedAdvances in Fuzzy Systems1687-71011687-711X2020-01-01202010.1155/2020/88355118835511T-S Fuzzy System Controller for Stabilizing the Double Inverted PendulumBoutaina Elkinany0Mohammed Alfidi1Redouane Chaibi2Zakaria Chalh3Laboratory of Engineering Systems and Applications, Sidi Mohammed Ben Abdellah University, Fez, MoroccoLaboratory of Engineering Systems and Applications, Sidi Mohammed Ben Abdellah University, Fez, MoroccoDepartment of Physics, Sidi Mohammed Ben Abdellah University, LESSI, Fez, MoroccoLaboratory of Engineering Systems and Applications, Sidi Mohammed Ben Abdellah University, Fez, MoroccoThis article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base. To achieve this objective, we designed a dynamic Lagrangian conceptualization of the double inverted pendulum and a state feedback representation based on the simple convex polytypic transformation. Finally, we used the fuzzy state feedback approach to linearize the mathematical nonlinear model and to develop a fuzzy controller H∞, given its great ability to simplify nonlinear systems in order to reduce the error rate and to increase precision. In our virtual conceptualization of the inverted pendulum, we used MATLAB software to simulate the movement of the system before applying a command on the upper part of the system to check its stability. Concerning the nonlinearities of the system, we have found a state feedback fuzzy control approach. Overall, the simulation results have shown that the fuzzy state feedback model is very efficient and flexible as it can be modified in different positions.http://dx.doi.org/10.1155/2020/8835511
collection DOAJ
language English
format Article
sources DOAJ
author Boutaina Elkinany
Mohammed Alfidi
Redouane Chaibi
Zakaria Chalh
spellingShingle Boutaina Elkinany
Mohammed Alfidi
Redouane Chaibi
Zakaria Chalh
T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum
Advances in Fuzzy Systems
author_facet Boutaina Elkinany
Mohammed Alfidi
Redouane Chaibi
Zakaria Chalh
author_sort Boutaina Elkinany
title T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum
title_short T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum
title_full T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum
title_fullStr T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum
title_full_unstemmed T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum
title_sort t-s fuzzy system controller for stabilizing the double inverted pendulum
publisher Hindawi Limited
series Advances in Fuzzy Systems
issn 1687-7101
1687-711X
publishDate 2020-01-01
description This article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base. To achieve this objective, we designed a dynamic Lagrangian conceptualization of the double inverted pendulum and a state feedback representation based on the simple convex polytypic transformation. Finally, we used the fuzzy state feedback approach to linearize the mathematical nonlinear model and to develop a fuzzy controller H∞, given its great ability to simplify nonlinear systems in order to reduce the error rate and to increase precision. In our virtual conceptualization of the inverted pendulum, we used MATLAB software to simulate the movement of the system before applying a command on the upper part of the system to check its stability. Concerning the nonlinearities of the system, we have found a state feedback fuzzy control approach. Overall, the simulation results have shown that the fuzzy state feedback model is very efficient and flexible as it can be modified in different positions.
url http://dx.doi.org/10.1155/2020/8835511
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