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

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 63
Author(s):  
Xiaodan Xu ◽  
Zhifeng Bai ◽  
Yuanyuan Shao
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Balance Equation ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Hydraulic Cylinder ◽  
Hydraulic Press ◽  
Amplitude Curve ◽  
Synchronous Control ◽  
Fuzzy Neural

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.

The Application Research of PLC Temperature Control System Based on Fuzzy Neural Network PID

2013 ◽  
Vol 340 ◽  
pp. 517-522
Author(s):  
Yong Wei Lu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Experimental Result ◽  
Temperature Control System ◽  
Pid Algorithm ◽  
Fuzzy Neural ◽  
Neural Network Pid

it is hard to establish the accurate model by using the traditional PID algorithm, and hard to adjust the system parameter in nonlinear system. In order to solve this problem, this paper proposed PID algorithm based on Fuzzy Neural Network. This algorithm combined PID algorithm, fuzzy control algorithm and neural network algorithm together, and formed one kind intelligent control algorithm. This paper designed and researched this algorithm, and applied in the PLC temperature control system. The experimental result indicated that the fuzzy neural network PID controller improved the controller quality, conquered some question such as variable parameter and nonlinear, and enhanced the systems robustness.

scholarly journals ALGORITHMS FOR KINEMATIC CONTROL OF MULTI-LINK MANIPULATION ROBOTS BASED ON A FUZZY NEURAL NETWORK

2021 ◽  
Vol 6 (6) ◽  
pp. 87-98
Author(s):  
P. Ganin ◽  
A. Kobrin
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Training Sample ◽  
Fuzzy Neural Networks ◽  
Control Algorithms ◽  
Membership Functions ◽  
Kinematic Control ◽  
Fuzzy Neural ◽  
Newton Raphson

The paper considers the possibility of constructing a kinematic control algorithm for manipulating robots with a serial-connected links. The construction of a control system based on a fuzzy neural network is proposed. The results of experimental studies on the selection of parameters of a fuzzy neural network in accordance with the set optimality criterion (in terms of speed), taking into account the subsequent iterative refinement by the Newton-Raphson method, are presented. The following network parameters are considered: the number and type of node membership functions, the size of the training sample with a different number of training approaches. An algorithm for forming a training sample for fuzzy neural networks is proposed in order to reduce the positioning error of the working body of the manipulating mechanism near the outer boundary of the workspace. The possibility of adapting the kinematic control algorithms by adjusting the parameters of the membership functions in the network nodes when performing the same type of tasks, based on the data of the Newton-Raphson refinement algorithm, is demonstrated. In the framework of this work, a comparative analysis of the developed kinematic control algorithm with algorithms based on iterative and neural network methods for solving the inverse kinematics problem of a manipulative robot is carried out. The conclusion is made about the increase in the speed for calculations of kinematic control algorithms while maintaining the required accuracy

scholarly journals Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 605 ◽  
Author(s):  
Wei Chen ◽  
Tongqing Xu ◽  
Junjie Liu ◽  
Mo Wang ◽  
Dean Zhao
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Variable Structure ◽  
Network Control ◽  
Servo Control ◽  
Mode Control ◽  
Fuzzy Neural

Through an analysis of the kinematics and dynamics relations between the target positioning of manipulator joint angles of an apple-picking robot, the sliding-mode control (SMC) method is introduced into robot servo control according to the characteristics of servo control. However, the biggest problem of the sliding-mode variable structure control is chattering, and the speed, inertia, acceleration, switching surface, and other factors are also considered when approaching the sliding die surface. Meanwhile, neural network has the characteristics of approaching non-linear function and not depending on the mechanism model of the system. Therefore, the fuzzy neural network control algorithm can effectively solve the chattering problem caused by the variable structure of the sliding mode and improve the dynamic and static performances of the control system. The comparison experiment is carried out through the application of the PID algorithm, the sliding mode control algorithm, and the improved fuzzy neural network sliding mode control algorithm on the picking robot system in the laboratory environment. The result verified that the intelligent algorithm can reduce the complexity of parameter adjustments and improve the control accuracy to a certain extent.

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