Synchronization Control Algorithm of Double-Cylinder Forging Hydraulic Press Based on Fuzzy Neural Network

Abstract

In order to solve the poor control accuracy problem of the traditional synchronous control algorithm for a double-cylinder forging hydraulic press, a synchronous control algorithm for double-cylinder forging hydraulic press based on a fuzzy neural network was proposed. According to the flow equation of valve and hydraulic cylinder, the balance equation and force balance equation of forging hydraulic cylinder are established by using the theory of electro-hydraulic servo systems, and the cylinder-controlled transfer function of forging hydraulic cylinder is deduced. By properly simplifying the transfer function, the mathematical model of synchronous control of double cylinder forging hydraulic press is established. According to the implementation process of traditional fuzzy neural networks, the properties of compensation operation are introduced. The traditional fuzzy neural network is optimized, and the optimized neural network is used to realize the synchronous control of the double cylinder forging hydraulic press. The experimental results show that the amplitude curve of the algorithm is very close to the expected amplitude curve, the error amplitude is only 0.3 mm, and the average control time is about 140 s, which fully shows that the algorithm has high accuracy and a good control effect.

Active Disturbance Rejection Position Synchronous Control of Dual-Hydraulic Actuators with Unknown Dead-Zones

“…In order to realize the synchronization motion between cables, adding two synchronization controllers in the cable space was proposed to solve the effect of cable tension during the motion control of cable-driven parallel robots [16]. Intelligent control methods (ICMs), which have strong nonlinear approximation ability and operate like the human brain, are also employed to enhance the synchronization control system's performance; examples include fuzzy control [17], fuzzy neural network control [18], and iterative learning control (ILC) [19]. Although these techniques have successfully improved the position synchronization control performance of each system, direct or indirect full state feedback and model information are required in almost all modern control theory methods.…”