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.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

Application of Blow-off Wind Tunnel Control Based on Genetic Algorithm Optimized BP-Neural Network PID Neural Network

2013 ◽  
Vol 310 ◽  
pp. 557-559 ◽  
Author(s):  
Li Ji ◽  
Xiao Fei Lian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Tunnel ◽  
Bp Neural Network ◽  
Control Method ◽  
Network Control ◽  
Operating Conditions ◽  
Algorithm Optimization ◽  
Large Delay ◽  
Neural Network Pid

For a blow-off tunnel running, there is the large delay and lag issues. We build a mathematical model of the wind tunnel Mach number control by the test modeling method, then analyse the pros and cons of various control methods based on BP neural network control algorithm. Put forward genetic algorithm optimization neural network adaptive control method to solve the large inertia of the wind tunnel system, and large delay. A large number of simulation studies, run a variety of operating conditions for the wind tunnel simulation proved that the improved adaptive neural network PID control method is reasonable and effective.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

2014 ◽  
Vol 945-949 ◽  
pp. 2266-2271
Author(s):  
Li Hua Wang ◽  
Xiao Qiang Wu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Network Control ◽  
Learning Ability ◽  
Space Environment ◽  
Laser Communication ◽  
Self Learning

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

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.

Research on Hydraulic Control System of Straightening Machine with Nine Rolls for Plates Based on BP Neural Network PID Controller

2013 ◽  
Vol 820 ◽  
pp. 117-121 ◽  
Author(s):  
Song Li ◽  
Jin Chun Song ◽  
Guan Gan Ren ◽  
Yan Cai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Mechanical Transmission ◽  
Hydraulic Control ◽  
Control Precision ◽  
Transmission Equipment ◽  
High Control ◽  
Neural Network Pid

A mechanical transmission equipment of traditional straightening machine for plates are driven by worm gear and worm, which causes small straightening force, slow pressing speed and low control precision. However, screwdown control system of straightening machine can be driven by hydraulic system, which will lead to large straightening force, rapid pressing speed and high control precision. This system was designed for straightening machine with nine rolls for plates, its transfer function was deduced, and the analysis on its stability and time response was conducted. A BP neural network PID controller was utilized in the system for improving dynamic characteristics. It can be concluded that the system responds rapidly, and stability and control precision are high if BP neural network PID controller is used in the system.

The Application of PSO-BP Neural Network PID Controller in Variable Frequency Speed Regulation System

2014 ◽  
Vol 599-601 ◽  
pp. 1090-1093 ◽  
Author(s):  
Chun Hua Li ◽  
Shao Xiong Xu ◽  
Yang Xie ◽  
Jie Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Speed Control ◽  
Regulation System ◽  
Particle Swarm Algorithm ◽  
Variable Frequency ◽  
Speed Control System ◽  
Neural Network Pid

Variable frequency speed control system hold good stability,more efficient,more energy conservation etc, so it has been widely used in the industrial areas,but the control strategy of traditional was difficult to achieve the desired control effect.This paper adopt particle swarm algorithm and BP neural network to construct the PID controller of PSO-BP neural network , the M-Files of PSO-BP neural network PID based on MATLAB through S-Function, and the mode of PSO-BP neural network PID variable frequency speed control system was established in SIMULINK platform.Simulation results show that the controller hold well robustness, follow and stability,and the dynamic characteristics of the original system was improved, the application value of this method in the variable frequency speed control system was proved.

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.

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