(c) ~

f3 3

f2 2 h2 h3

2780

Fig。 6 Optimization parameters with PSO

Fig。 7 Trolley position tracking curve

Fig。 8 Swing angle tracking curve

Fig。 9 Rope length tracking curve

J。  Cent。  South  Univ。  (2012)  19:  2774−2781

Fig。 10 Trolley position curve with positioning disturbance

Fig。 11 Load angle curve with positioning disturbance

Fig。 12 Trolley position curve with swinging disturbance

Fig。 13 Load angle curve with swinging disturbance

J。 Cent。 South Univ。 (2012) 19: 2774−2781 2781

cranes [J]。 Control Engineering Practice, 2007, 15(7): 825−837。

7Conclusions

1)A new intelligent anti-swing control scheme is proposed with combination of SMC’s robustness and FNN’s independence on system model。 The bridge crane is simplified into three multi-input subsystems, four sliding mode surfaces are defined and fuzzy neural networks sliding controller is designed。 It is capable of tackling non-linear system with parameter uncertainties。

2)Compared with conventional sliding mode control, the system achieves good positioning accuracy and significant sway reduction with considering changes of lifting-rope when the bridge crane system model has uncertainties and disturbance。

3)Moreover, by the PSO algorithm, the parameters of controller are optimized to accelerate system convergence, the inherent chattering phenomena of sliding mode control can be eliminated and the performances of control system can be ameliorated。 The simulation results show that the correctness and validity of this method。

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[1] CHO S K, LEE H H。 A fuzzy-logic antiswing controller for three-