Adaptive Neural-Fuzzy controller design combined with LQR to control the position of gantry crane




Gantry crane, Adaptive neural-fuzzy controller, LQR control, Position control, Oscillation control


As the world grows, the demand for transporting goods is increasing, the number of goods in factories and ports is increasing, to transport all these goods, cranes are indispensable. In fact, currently, crane rigs working in factories and ports operate with low stability, when working or the phenomenon of swaying of the load occurs, leading to inaccurate positioning, loss of safe transportation of goods. To overcome these shortcomings, the paper proposes the design of a neural-fuzzy adaptive controller combined with an LQR controller (ANFIS-LQR) to control the forklift's position in the shortest time to achieve the desired exact position. At the same time, we want to control the deflection angle of the load so that the vibration when working is minimal. To check and evaluate the quality and stability of the system; the proposed design controller is simulated on MATLAB/Simulink software in the case of changes in system parameters and noise affecting the gantry crane system. To evaluate the superiority of the paper compared with published works, the author compares ANFIS-LQR with other published control methods such as DE-PID, Fuzzy-PD, Fuzzy dual and Fuzzy sliding, the simulation results show that the neural-fuzzy adaptive controller combined with the proposed LQR controller works well t_xlvt=2.1s , t_xlgt=3.5s, 0max=0.3(rad).


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J. Smoczek, Interval arithmetic-based fuzzy discrete-time crane control scheme design, Bull. Pol. Ac.: Tech. 61 (4), 2013, pp. 863–870.

N. Sun, Y.C. Fang, and X.B. Zhang, Energy coupling output feedback control of 4-DOF underactuated cranes with saturated inputs, Automatica 49 (5), 2013, pp. 1318–1325.

Khalid L. Sorensen, William Singhose, Stephen Dickerson, A controller enabling precise positioning and sway reduction in bridge and gantry cranes, Control Engineering Practice 15, 2007, pp. 825–837.

Quang Hieu Ngo and Keum-Shik Hong, Sliding-Mode Antisway Control of an Offshore Container Crane, IEEE/ASME Transactions on Mechatronics, VOL. 17, NO. 2, APRI, 2012.

Mohammad Javad Maghsoudi, Z. Mohamed, A.R. Husain, M.O. Tokhi, An optimal performance control scheme for a 3D crane, Mechanical Systems and Signal Processing 66-67, 2016,pp. 756–768.

Zhe Sun, Ning Wang, Yunrui Bi, Jinhui Zhao, A DE based PID controller for two dimensional overhead crane, Proceedings of the 34th Chinese Control Conference July 28-30, 2015, Hangzhou, China.

Ning Sun, Yongchun Fang, Xuebo Zhang, Energy coupling output feedback control of 4-DOF underactuated cranes with saturated inputs, Automatica 49, 2013, pp. 1318–1325.

Naif B. Almutairi and Mohamed Zribi, Fuzzy Controllers for a Gantry Crane System with Experimental Verifications, Article in Mathematical Problems in Engineering, 2016, DOI: 10.1155/1965923.

Lifu Wang, Hongbo Zhang, Zhi Kong, Anti-swing Control of Overhead Crane Based on Double Fuzzy Controllers, IEEE Chinese Control and Decision Conference (CCDC), 2015, 978-1-4799-7016-2/15/$31.00.

Dianwei Qian, Jianqiang Yi, Dongbin Zhao, Control of Overhead Crane Systems by Combining Sliding Mode with Fuzzy Regulator, Milano (Italy) August 28 - September 2, 2011.

Yu Zhangguan, Modern Control Theory [M], Harbin: Harbin Institute of Technology Press, 2005.

Xue Dingyu, Chen Yangquan, System Simulation Technology and Application Based on MATLAB / Simulink [M], Beijing, 2009.

Dervis Karaboga & Ebubekir Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, An International Science and Engineering Journal, 2019




How to Cite

Dinh Do VAN, “Adaptive Neural-Fuzzy controller design combined with LQR to control the position of gantry crane”, J. Appl. Methods Electron. Comput., vol. 11, no. 2, pp. 94–100, Jun. 2023.



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