Study of PSO Optimized BP Neural Network and Smith Predictor for MOCVD Temperature Control in 7 nm 5G Chip Process

2020 ◽  
pp. 568-576 ◽  
Author(s):  
Kuo-Chi Chang ◽  
Yu-Wen Zhou ◽  
Hsiao-Chuan Wang ◽  
Yuh-Chung Lin ◽  
Kai-Chun Chu ◽  
...  
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Smith Predictor

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.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

System of Annealing Furnace Temperature Control Based on Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 881-884
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nonlinear Approximation ◽  
Annealing Furnace ◽  
Furnace Temperature ◽  
Control Precision ◽  
High Control ◽  
Adaptive Ability

In the thesis the adaptive ability of neural network strong and good nonlinear approximation ability, A controller is designed based on BP neural network by the adaptive ability of neural network strong and good nonlinear approximation ability in this paper, this method changed defect of the usual PID controller that parameters of annealing furnace condition are not easy set and the ability to adapt is poor. The new method is not only has good stability, but also has high control precision and strong adaptability.

Smith Predictor Based on Predicting Induced-Delay

2011 ◽  
Vol 201-203 ◽  
pp. 2003-2006
Author(s):  
Shu De Li ◽  
Yi Chen ◽  
Cai Xia Liu
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Control System ◽  
Bp Neural Network ◽  
Networked Control System ◽  
Smith Predictor ◽  
Networked Control ◽  
Object Model ◽  
Random Delay

Since communication network is introduced into control system, induced-delay appears. Because of the delay, the performance of networked control system becomes bad, even unsteady. Conventional Smith predictor is sensitive to error in object model and needs delay’s value in advance. Regarding random delay, its application is limited. In this paper, we propose a method based on induced-delay predicted by BP neural network, which use two historical delay values to predict the next one. Smith predictor adjusts its parameters according to that value in time. The simulating results indicate that the precision of delay-predicting can be ensured and the performance of networked control system has been improved.

Based on the BP Neural Network VAc Synthesis Reactor in Temperature Control

2013 ◽  
Vol 313-314 ◽  
pp. 1389-1392
Author(s):  
Hua Meng ◽  
Jia Ma ◽  
Shi Feng Bao ◽  
Shao Qing Wei
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Synthesis Process ◽  
Neural Network Modeling ◽  
Structure Model ◽  
Medium Temperature ◽  
Reactor Temperature

This paper analyzes the synthesis of vinyl acetate production process and technology, and applied the artificial neural network modeling approach, by using the adaptive learning rate BP learning algorithm, then established the medium temperature control of BP neural network in the structure model of the VAc synthesis process of flurried bed synthesis reactor, by controlling medium temperature of the synthesis reactor, the results show that: Be able to effectively will in reactor temperature control in good accuracy.

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