Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane

Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane

The dynamic model of overhead crane is highly nonlinear and uncertain. In this paper, Takagi-Sugeno (T-S) fuzzy modeling and PSO-based robust linear quadratic regulator (LQR) are proposed for anti-swing and positioning control of the system. First, on the basis of sector nonlinear theory, the two T-...

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Main Authors: Xuejuan Shao, Jinggang Zhang, Xueliang Zhang
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2019/4596782
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spelling doaj-ae0e6cdc477a4c17a91cff8320a59aca2020-11-24T21:03:47ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/45967824596782Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead CraneXuejuan Shao0Jinggang Zhang1Xueliang Zhang2College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaCollege of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaCollege of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaThe dynamic model of overhead crane is highly nonlinear and uncertain. In this paper, Takagi-Sugeno (T-S) fuzzy modeling and PSO-based robust linear quadratic regulator (LQR) are proposed for anti-swing and positioning control of the system. First, on the basis of sector nonlinear theory, the two T-S fuzzy models are established by using the virtual control variables and approximate method. Then, considering the uncertainty of the model, robust LQR controllers with parallel distributed compensation (PDC) structure are designed. The feedback gain matrices are obtained by transforming the stability and robustness of the system into linear matrix inequalities (LMIs) problem. In addition, particle swarm optimization (PSO) algorithm is used to overcome the blindness of LQR weight matrix selection in the design process. The proposed control methods are simple, feasible, and robust. Finally, the numeral simulations are carried out to prove the effectiveness of the methods.http://dx.doi.org/10.1155/2019/4596782
collection DOAJ
language English
format Article
sources DOAJ
author Xuejuan Shao
Jinggang Zhang
Xueliang Zhang
spellingShingle Xuejuan Shao
Jinggang Zhang
Xueliang Zhang
Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane
Mathematical Problems in Engineering
author_facet Xuejuan Shao
Jinggang Zhang
Xueliang Zhang
author_sort Xuejuan Shao
title Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane
title_short Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane
title_full Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane
title_fullStr Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane
title_full_unstemmed Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane
title_sort takagi-sugeno fuzzy modeling and pso-based robust lqr anti-swing control for overhead crane
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2019-01-01
description The dynamic model of overhead crane is highly nonlinear and uncertain. In this paper, Takagi-Sugeno (T-S) fuzzy modeling and PSO-based robust linear quadratic regulator (LQR) are proposed for anti-swing and positioning control of the system. First, on the basis of sector nonlinear theory, the two T-S fuzzy models are established by using the virtual control variables and approximate method. Then, considering the uncertainty of the model, robust LQR controllers with parallel distributed compensation (PDC) structure are designed. The feedback gain matrices are obtained by transforming the stability and robustness of the system into linear matrix inequalities (LMIs) problem. In addition, particle swarm optimization (PSO) algorithm is used to overcome the blindness of LQR weight matrix selection in the design process. The proposed control methods are simple, feasible, and robust. Finally, the numeral simulations are carried out to prove the effectiveness of the methods.
url http://dx.doi.org/10.1155/2019/4596782
work_keys_str_mv AT xuejuanshao takagisugenofuzzymodelingandpsobasedrobustlqrantiswingcontrolforoverheadcrane
AT jinggangzhang takagisugenofuzzymodelingandpsobasedrobustlqrantiswingcontrolforoverheadcrane
AT xueliangzhang takagisugenofuzzymodelingandpsobasedrobustlqrantiswingcontrolforoverheadcrane
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