scholarly journals A Pressure Control Method for Emulsion Pump Station Based on Elman Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chao Tan ◽  
Nan Qi ◽  
Xin Zhou ◽  
Xinhua Liu ◽  
Xingang Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Pressure Control ◽  
Control Model ◽  
System Framework ◽  
Elman Neural Network ◽  
Pump Station ◽  
Comparison Results ◽  
Safety Production ◽  
Key Techniques

In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

Research on Pressurizer Pressure Control System Based on BP Neural Network Control of Self-Adjusted PID Parameters

2013 ◽  
Vol 291-294 ◽  
pp. 2416-2423 ◽  
Author(s):  
Guo Duo Zhang ◽  
Xu Hong Yang ◽  
Dong Qing Lu ◽  
Yong Xiao Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nuclear Reactor ◽  
Control Method ◽  
Setting Time ◽  
Pressure Control ◽  
Network Control ◽  
Pressure System

The pressurizer is an important device in nuclear reactor system, and the traditional PID regulator is usually used to control pressure system of pressurizer in modern reactors. However, it is difficult to get precise parameters of traditional PID controller, and the PID control method is relied on the precise mathematical model badly. And the response of PID controller is often shown by the large amount of overshoot and long setting time which are not the desired results. For such a large inertia and complex time-varying control system, the tradition PID controller can not obtain the satisfy control results. A controller based on BP neural network in this paper has a simple structure, and the parameters of PID controller can be tuned on-line by the neural network self-learning characteristics. The computer simulation experiment demonstrates that the BP neural network PID controller performs very well when compared with the tradition PID regulator in minimal overshoot and more quick response.

Applied of RBF Neural Network in Pressure and Flow Control System

2015 ◽  
Vol 713-715 ◽  
pp. 909-914
Author(s):  
Fei Wang ◽  
Xiao Hua Sun ◽  
Zhi Jun Zou ◽  
Hao Li
Keyword(s):  
Neural Network ◽  
Flow Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Control Process ◽  
Mathematic Model ◽  
Pressure Control ◽  
Water Pipe ◽  
Object A ◽  
Pid Algorithm

This paper is addressed water pipe pressure control method, chose a water pipe with bypass valve as an object. The paper made the pressure and flow control of the object a mathematic model. This paper design a PID control algorithm which base on RBF neural network to control the pressure and flow of the object, and simulate the control process both RBF neural network PID algorithm and PID algorithm. The last part of the paper contrasts the two simulation result.

RBF-Elman Neural Network Control on Electro-Hydraulic Load Simulator

2013 ◽  
Vol 433-435 ◽  
pp. 1054-1060
Author(s):  
Xiao Hua Wang ◽  
Shuai Wu ◽  
Zong Xiao Jiao
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Control Method ◽  
Back Propagation ◽  
Network Control ◽  
Elman Neural Network ◽  
Simulation Performance ◽  
Neural Network Controller ◽  
Load Simulator ◽  
Load Simulation

Due to load simulation system existing strong disturbance, parameters time-variation and nonlinear, there was low control precision, poor adaptive ability and robustness in traditional control algorithm. In order to improve load simulation performance, The RBF-Elman neural network-based adaptive control method is presented. In this way, the load simulator system is identified by the RBF-Elman neural network identifier, which provides model information (Jacobian matrix) to the PID controller and synchronous compensator in order to make it adaptive. Back-propagation algorithms are used to train neural network. The PID controller which is designed by requirement for steady can overcome the shortcoming of the neural network controller. Finally, the simulations confirm that this control scheme results in a quick response, robustness, and excellent ability against disturbance.

scholarly journals Neural Network Control for Trajectory Tracking and Balancing of a Ball-Balancing Robot with Uncertainty

2021 ◽  
Vol 11 (11) ◽  
pp. 4739
Author(s):  
Hyo-Geon Jang ◽  
Chang-Ho Hyun ◽  
Bong-Seok Park
Keyword(s):  
Neural Network ◽  
Control System ◽  
Trajectory Tracking ◽  
Control Method ◽  
Tracking Error ◽  
System Structure ◽  
Underactuated System ◽  
Dynamic Surface ◽  
Comparison Results ◽  
Balancing Robot

In this paper, a neural-network-based control method to achieve trajectory tracking and balancing of a ball-balancing robot with uncertainty is presented. Because the ball-balancing robot is an underactuated system and has nonlinear couplings in the dynamic model, it is challenging to design a controller for trajectory tracking and balancing. Thus, various approaches have been proposed to solve these problems. However, there are still problems such as the complex control system and instability. Therefore, the objective of this paper was to propose a solution to these problems. To this end, we developed a virtual angle-based control scheme. Because the virtual angle was used as the reference angle to achieve trajectory tracking while keeping the balance of the ball-balancing robot, we could solve the underactuation problem using a single-loop controller. The radial basis function networks (RBFNs) were employed to compensate uncertainties, and the controller was designed using the dynamic surface control (DSC) method. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded. Therefore, the control system structure was simple and ensured stability in achieving simultaneous trajectory tracking and balancing of the ball-balancing robot with uncertainty. Finally, the simulation results are given to verify the performance of the proposed controller through comparison results. As a result, the proposed method showed a 19.2% improved tracking error rate compared to the existing method.

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