Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection

Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy...

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Vydáno v:2011 IEEE International Conference on Control System, Computing and Engineering s. 306 - 311
Hlavní autoři: Adeli, M., Zarabadipour, H., Zarabadi, S. H., Shoorehdeli, M. A.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2011
Témata:
Conferences
Control systems
Cranes
Genetic algorithm
Genetic algorithms
linear matrix inequality
Linear Quadratic Regulation
Load modeling
Mathematical model
Nonlinear systems
overhead crane
parallel distributed compensation
Takagi_Sugeno fuzzy modeling
ISBN:9781457716409, 1457716402
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Shrnutí:Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.
ISBN:9781457716409
1457716402
DOI:10.1109/ICCSCE.2011.6190542