Mechanical Engineering Science

Research on Dual-motor Synchronization Based on Fuzzy Neural PID Control

WUXiaoqiang (Ordos INSTITUTE OF TECHNOLOGY College of Mechanical and Transportation Engineering)

Abstract


In order to solve the problem of double motor synchronous error in the hydraulic lifting system of large crane, fuzzy control and neural network control are combined to realize the dynamic correction of PID parameters. With the use of cross-coupling control method in the control process based on the dynamic characteristics of the hydraulic system,  both the pressure difference of hydraulic motor outlet and displacement of steel wire rope are regard as control index on the simulation and experimental research to improve the accuracy of synchronous control. The results show that this control strategy has strong ability of anti-interference, and effectively improving the synchronization control precision of the two motors.

Keywords


Crane; fuzzy neural network; Cross coupling; Synchronous control

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References


HUANG Liqin, LIU Rong, CHEN Ying. Research on pipeline pressure shock in valve control hydraulic positioning system [J]. Mechanical & Electrical Engineering Magazine, 2009,22(3):234-236.

Favennec G, Alirand M. Optimal response of pressure reducer and stability influence of the downstream line dynamics [J]. Modena, Italy, 2002(7):1-10.

S.Guo,L.Huang. Periodic oscillation for discrete-time Hopfield neural networks [J]. Physics Lettesr A,2004,329(3):199-206.

Li Jun-wei, Zhao Ke-ding, Wu Sheng-lin. Re- search on Dual Electro hydraulic Motors Synch- ronization via Fuzzy Control [J]. Machine Tool & Hydraulics,2003,25 (1):115-116.

ZHAO L,LIU X H,WANG T J. Influence of counterbalance valve parameters on stability of the crane lifting system[C]. International Conference on Mechatronics and Automation Xi’an, China, 2010.

Lu Ren, James K. Mills,Dong Sun. Adaptive Synchronized Control for a Planar Parallel Manipulator [J]. Theory and Experiments, 2006, 128(4):976-979.

K.Hirasawa, S.Mabu, J.Hu. Propagation and control of stochastic signals through universal learning networks [J]. Neural Networks, 2006,19(4): 487-499.

Di Zhou,Tielong Shen,Katsutoshi Tamura.Adaptive Nonlinear Synch- ronization Control of Twin-Gyro Precession [J]. Journal of Dynamic Systems, Measurement and Control,2006,128(3):592-599.

QU J Y,REN C B,et al.Parameters optimization method for variable displacement pump/motor and transmission of hydraulic braking energy regeneration system [J]. International Forum on Computer Science Technology and Applications,2009(3):19-22.

S.Guo, L.Huang. Periodic oscillation for discrete-time Hopfield neural networks [J]. Physics Lettesr A, 2004,329(3):199-206.

Kayacan E, Cigdem O, Kaynak O. Sliding mode control approach for online learning as applied to type-2 fuzzy neural networks and its experimental evaluation [J]. IEEE Transactions on Industrial Electronics, 2012,59(9):3510-3520.

AHN K W, HYUN J H.Optimization of double loop control parameters for a variable displacement hydraulic motor by genetic algorithms [J]. JSME, International Journal Series C-Mechanical Systems Machine Elements and Manufacturing,2 005,48(1):81-86.




DOI: https://doi.org/10.33142/mes.v7i2.18368

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