The Neural Network Control Algorithm Research of Single Crystal Furnace Temperature System

2013 ◽  
Vol 765-767 ◽  
pp. 789-792
Author(s):  
Xin Song ◽  
Ming Yu Li ◽  
Li Li ◽  
Chao Yang
Keyword(s):  
Neural Network ◽  
Single Crystal ◽  
Control Strategies ◽  
Time Lag ◽  
Adaptive Method ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Modeling ◽  
Neural Network Control ◽  
Long Time

Straight pull single crystal furnaces temperature control system has problem of the long time lag and nonlinearity, so the precise mathematic mode that is hard to build. Advanced control strategies show strong advantages for resolving these problems. This paper use artificial neural network modeling approach to establish single crystal furnace temperatures neural network control BP structure model, use adaptive method to control the temperature of the single crystal furnace.

Research of Intelligent Control Policy on Main Steam Temperature of Thermal Power Plant

2011 ◽  
Vol 143-144 ◽  
pp. 307-311 ◽  
Author(s):  
Yu Feng Luo ◽  
Lu Lu Liu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Strategies ◽  
Thermal Power ◽  
Network Control ◽  
Neural Network Control ◽  
Steam Temperature ◽  
Boiler Load ◽  
Fuzzy Neural ◽  
Main Steam

In view of the nonlinear, time-variable, long delay, large inertia character of main steam temperature system, the difficult point of control is summarized. The status quo of application study on main steam temperature by fuzzy control, neural network and fuzzy neural network control, genetic algorithms is introduced. And taking "the 600 MW concurrent boiler load in 100%" as an example, carry on the main steam temperature control simulation by means of Matlab/Simulink software. In this simulation, four control strategies are taking for the simulation experiment, and comparing with the traditional PID control algorithm. The simulation results prove that fuzzy neural network control strategies have good robustness, fast response, short setting time, and great potential for the control of main steam temperature.

scholarly journals A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1365
Author(s):  
Yuan Liu ◽  
Song Xu ◽  
Seiji Hashimoto ◽  
Takahiro Kawaguchi
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Reference Model ◽  
Mimo System ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Model Based ◽  
Neural Network Controller ◽  
Highly Nonlinear

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

Improved CMAC Neural Network Control for Superheated Steam Temperature

2011 ◽  
Vol 383-390 ◽  
pp. 111-117 ◽  
Author(s):  
Li Jun Chen ◽  
Bo Sun ◽  
Jian Chao Diao ◽  
Li Li Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Superheated Steam ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Steam Temperature ◽  
Cmac Neural Network ◽  
Superheated Steam Temperature

Aiming at that superheated steam temperature system exists the large inertia and large time delay of the dynamic characteristics,and the converge speed of the conventional CMAC neural network is not fast enough to the real-time system, a credit assignment CMAC (CA-CMAC) neural network control is adopted in superheated steam temperature control system, which is proposed to speed up the learning process in CMAC. The simulation of the superheated steam temperature control system shows that CA-CMAC converges faster than the conventional CMAC. This result illustrates the effectiveness of this method.

The Temperature Control System Research of PVC Latex Batching Based on PLC

2013 ◽  
Vol 299 ◽  
pp. 97-101
Author(s):  
Hua Meng ◽  
Ming Yu Li ◽  
Jie Zhu ◽  
Yan Ju Zhang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Design Method ◽  
Control Strategies ◽  
Time Lag ◽  
Temperature Control System ◽  
System Research ◽  
Advanced Control ◽  
Long Time

The PVC latex batching system has problem of the long time lag and nonlinearity and the precise mathematic mode that is hard to build. Advanced control strategies show strong advantages for resolving these problems, thus the combination of the advanced control strategies with the popular PLC has been the hot area in controlling-industry. In this paper, it proposed to apply the fuzzy control to the Batching system and has completed the design of the fuzzy controller. Based on the characteristics of the PLC, the realization of fuzzy control on PLC and the design method of the fuzzy controller in Siemens S7-300 PLC have been put forward.

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