Optimization of the Hydraulic Control System Utilizing BP Neural Network Control Strategy Based on Genetic Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1245-1252
Author(s):  
Ying Ying Feng ◽  
Nan Mu Hui ◽  
Zong An Luo ◽  
Dian Hua Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Hydraulic System ◽  
Learning Ability ◽  
Hydraulic Control ◽  
Control Effect ◽  
Hydraulic Control System

For the characteristic of the MMS series Thermo-Mechanical Simulator hydraulic control system, using traditional PID control method can not achieve the desired control effect. Basing on genetic algorithm, BP neural network, which has the arbitrary non-linear approximation ability, self-learning ability and generalization ability, has been used into the hydraulic control system to achieve the online adjustment of the weighting coefficients and the adaptive adjustment of PID control parameters. The results of simulation and online tests show that the control effect of hydraulic system has been improved significantly, and the accurate control of hydraulic system hammer displacement has been realized.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

The Kinematic and Dynamic Analysis of Hydraulic Control System Based on the Lagrangian Force Method

2018 ◽  
Vol 15 (05) ◽  
pp. 1850041
Author(s):  
Thi-Na Ta ◽  
Cao-Sang Tran ◽  
Yunn-Lin Hwang
Keyword(s):  
Control System ◽  
Dynamic Analysis ◽  
Hydraulic System ◽  
Lagrange Equation ◽  
Control Parameters ◽  
Hydraulic Control ◽  
Hydraulic Systems ◽  
Dynamical Parameters ◽  
Characteristic Effect ◽  
Hydraulic Control System

In this paper, the hydraulic control system design and dynamic analysis are considered in superior calculation efficiency based on Lagrangian method and fundamental laws for getting control algorithms to investigate about hydraulic system how to control hydraulic system and take hydraulic controller to the optimization. The systems built in dynamical parameters are associated hydraulic control system achieved with control parameters. And then, an interface is achieved. The relations between control parameters are obtained. The numerical tools are going to determine all kinematic parameters of bodies such as displacement, velocities, accelerations as well as reaction forces are responded. The AMESim and RecurDyn are used such Computer Aided Engineering (CAE) techniques to solve the numerical examples in this paper. Those examples will imply that the equation of motion based on Euler–Lagrange equation and characteristic effect of force is executable. Through results, we could achieve control improvement at different hydraulic systems.

The Research and Design of Liquid Level Control System Based on Computer Nerve Network

2012 ◽  
Vol 591-593 ◽  
pp. 1629-1632
Author(s):  
Li Zhang ◽  
Jian Hui Wang ◽  
Hou Yao Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Liquid Level ◽  
Level Control ◽  
Control Effect ◽  
Nerve Network ◽  
Level Control System ◽  
Pid Neural Network ◽  
Liquid Level Control

This thesis mainly elaborated the PID neural network feed-forward algo-rithm and back propagation algorithm and the structure form of its controller, then make use of MATLAB to simulate the liquid level adjusting system, analysis its control perform-ance and choose appropriate neural network parameters, and compared with the traditional PID control effect, analyzes the advantages of PID neural network. Through the comparison with the conventional PID control, PID neural network is superior to the traditional PID. The traditional PID control tuning parameters has a large number of thumb rules for reference, but the setting out of the parameters is not necessarily good. And sometimes we have to modify the parameters if we wound the better control effect. PID neural network is set up as long as the learning step in accordance with the PID rule set. this paper has show that Liquid Level Control System based on Computer Nerve Network has good control effect of rapid and effective.

Design and Analysis of the Low-Speed Hydraulic Control System of 3.15MN Forging Press

2013 ◽  
Vol 690-693 ◽  
pp. 2210-2217
Author(s):  
Yi Bo Li ◽  
Ming Hui Huang ◽  
Qing Pan ◽  
Min Chen
Keyword(s):  
Control System ◽  
Hydraulic System ◽  
Hydraulic Press ◽  
System Structure ◽  
Hydraulic Control ◽  
Low Speed ◽  
Hydraulic Theory ◽  
Forging Press ◽  
The Mathematical Model ◽  
Hydraulic Control System

A low speed hydraulic control system applying to 3.15MN forging press isdesigned for isothermal forging processes. Based on the hydraulic theory andthe system structure, both the mathematical model and the simulation model ofthe hydraulic press are established with its features discussed. PID controlmethod is adopted and genetic algorithm is used to automatically tune theparameters of the controller to deal with nonlinearity and time-variant of thecontrolled plant. Co-simulation of the control system and hydraulic system isestablished to verify the feasibility of the design. It is proved by anisothermal forging of a turbine disc that the system has good performances andcan achieve an ultra speed control in the end.

Research on Self-Tuning PID Control Based on BP Neural Network of Multi-Motor Synchronous Control System

2013 ◽  
Vol 19 (6) ◽  
pp. 1668-1671 ◽  
Author(s):  
Huang Ke ◽  
Yu Zhixiong ◽  
Dong Qiang ◽  
Liu Jishun ◽  
Lu Le ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Synchronous Control ◽  
Self Tuning

Design and Analysis of Hydraulic Control System for Pipe Storage and Handling System

2011 ◽  
Vol 480-481 ◽  
pp. 1240-1245
Author(s):  
Yu Wang ◽  
Bao Lin Liu ◽  
Yuan Biao Hu
Keyword(s):  
Control System ◽  
Hydraulic System ◽  
Hydraulic Pressure ◽  
Hydraulic Control ◽  
Round Trip ◽  
Handling System ◽  
Piston Speed ◽  
Valve Operation ◽  
Hydraulic Control System ◽  
Pipe Joint

This paper research the hydraulic control system of pipe storage and handling system. The pipe storage and handling system (PSHS) is the key parts on rig to handle the drilling pipe and it can improve the efficiency during the drilling works. The hydraulic system of pipe storage and handling system have designed according to automatic round-trip operation in drilling engineering. The mathematical modeling of hydraulic components in PSHS have established and analyzed. The simulation model of hydraulic system of PSHS is also built and simulated to analysis the characteristics of reversal valve operation, Hydraulic pressure adjusting operation, piston speed control and state of accumulator based on the AMESim. The results prove that the operations of pipe storage and handle such as pipe storage, pipe clamping, pipe lifting, pipe transfer and pipe joint can be accomplished drive by hydraulic system. Meanwhile, AMESim have great views to research the geosciences equipment.

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