4. Conclusion

In this chapter an adaptive robust controller which can adapt with the system uncertainties and robust to the external disturbances is establishes based on Euler–Lagrange model of the overhead crane system. Using this controller, the error dynamic of the system is show in the form of state space model. By using Lyapunov theory, it is shown that the overall system is input-to-state stable. The proposed robust adaptive controller is verified through the Matlab/ Simulink toolbox under the three conditions, i.e., nominal system parameters, variation system parameters, and external disturbances. The simulation results indicate that the presented scheme gives the good performances for the overhead crane system (fast response, small swing angle in the transient time and no swing angle in the steady state, no position error) even that the system is uncertainties and existing the external disturbances.

[7] Bartolini G, Pisano A, Usai E. Second-order sliding-mode control of container cranes.

Robust Adaptive Control of 3D Overhead Crane System http://dx.doi.org/10.5772/intechopen.72768 347

[8] Sun N, Fang Y, Chen H. A new antiswing control method for underactuated cranes with unmodeled uncertainties: Theoretical design and hardware experiments. IEEE Transac-

[9] Liu D, Yi J, Zhao D, Wang W. Adaptive sliding mode fuzzy control for a two-dimensional

[10] Chen W, Saif M. MIMO nonlinear systems using high-order sliding-mode differentiators with application to a laboratory 3-D crane. IEEE Transactions on Industrial Electronics.

[11] Ngo QH, Hong K-S. Sliding-mode antisway control of an offshore container crane. IEEE/

[12] Tuan LA, Moon S-C, Lee WG, Lee S-G. Adaptive sliding mode control of overhead cranes with varying cable length. Journal of Mechanical Science and Technology. 2013;27(3):

[13] Almutairi NB, Zribi M. Sliding mode control of a three-dimensional overhead crane.

[14] Li C, Lee CY. Fuzzy motion control of an auto-warehousing crane system. IEEE Trans-

[15] Cho SK, Lee HH. A fuzzy-logic antiswing controller for threedimensional overhead

[16] Liang YC, Koh KK. Concise anti-swing approach for fuzzy crane control. Electronics

[17] Chang CY. Adaptive fuzzy controller of the overhead cranes with nonlinear disturbance.

[18] Zhao Y, Gao H. Fuzzy-model-based control of an overhead crane with input delay and actuator saturation. IEEE Transactions on Fuzzy Systems. 2002;20(1):181-186

[19] Smoczek J. Experimental verification of a GPC-LPV method with RLS and P1-TS fuzzy based estimation for limiting the transient and residual vibration of a crane system.

[20] Park M-S, Chwa D, Hong S-K. Antisway tracking control of overhead cranes with system uncertainty and actuator nonlinearity using an adaptive fuzzy sliding-mode control.

[21] Chang C-Y, Chiang K-H. Intelligent fuzzy accelerated method for the nonlinear 3-D crane

Automatica. 2002;38(10):1783-1790

2008;55(11):3985-3996

885-893

tions on Industrial Electronics. 2015;62(1):453-465

overhead crane. Mechatronics. 2005;15(5):505-522

ASME Transasction on Mechatronics. 2012;17(2):201-209

Journal of Vibration and Control. 2009;15(11):1679-1730

IEEE Transactions on Industrial Informatics. 2007;3(2):164-172

Mechanical Systems and Signal Processing. 2015;62-63:324-340

IEEE Transactions on Industrial Electronics. 2008;55(11):3972-3984

control. Expert Systems with Applications. 2009;36(3):5750-5752

actions on Industrial Electronics. 2001;48(5):983-994

cranes. ISA Transactions. 2002;41(2):235-243

Letters. 1997;3(2):167-168
