(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。

References

[6]  YANG Jung-hua, YANG Kuang  Shine。  Adaptive  coupling   control for overhead crane systems [J]。 Mechatronics, 2007, 17(2/3): 143− 152。

[7] ZIYAD N M, MOHAMMED F D。 A graphical design of an input-shaping controller for quay-side container cranes with large hoisting:theory and experiments [J]。 Jordan Journal of Mechanical and Industrial Engineering, 2007, 1(1): 57−67。

[8] SINGHOSE W, KAMOI T, TAURA A。 Radial-motion assisted command shapers for nonlinear tower crane rotational slewing [J]。 Control Engineering Practice, 2010, 18(5): 523−531。

[9] NAOKI U。 Robust control of rotary crane by partial-state feedback with integrator [J]。 Mechatronics, 2009, 19(8): 1294−1302。

[10] MAHMUD I S, WAHYUDI。 Sensorless anti-swing control for automatic gantry crane system: Model-based approach [J]。 International Journal of Applied Engineering Research, 2007, 2(1): 147−161。

[11]    YU W, ARMENDARIZ M A, RODRIGUEZ F O。 Stable adaptive

compensation with fuzzy CMAC for an overhead crane [J]。 Information Sciences, 2011, 181(21): 4895−4907。

[12] ZHANG Xi-zheng, WANG Yao-nan。 Robust fuzzy sliding-mode control for T-S model based permanent magnet synchronous motor [J]。 Journal of Central South University: Science and Technology, 2009, 40(S1): 68−73。

[13] ZHANG Zhi-gang, ZHANG Gui-xiang。 Combined control of sliding mode variable structure vector for permanent magnet synchronous wind power system [J]。 Journal of Central South University: Science and Technology, 2011, 42(7): 1986−1991。(in Chinese)

[14] LIU Dian-tong, YI Jian-qiang, ZHAO Dong-bin, WANG Wei。 Swing-Free  transporting  of  two-dimensional  overhead  crane using

[1] CHO S K, LEE H H。 A fuzzy-logic antiswing controller for three- 桥式起重机智能防摆控制英文文献和中文翻译(10):http://www.youerw.com/fanyi/lunwen_97253.html