Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Delay System ◽  
Temperature Control System ◽  
Control Effect ◽  
Rolling Optimization ◽  
Hot Blast ◽  
Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

Application of Fuzzy Predictive Control in Grain Drying Coal Burning Hot Air Furnace Temperature Control System

2014 ◽  
Vol 530-531 ◽  
pp. 981-984
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Fast Response ◽  
Delay System ◽  
Temperature Control System ◽  
Large Delay ◽  
Blast Stove ◽  
Grain Drying ◽  
Hot Blast ◽  
Fuzzy Predictive Control

Grain drying with chain coalfired hot blast stove for temperature stable and economic operation.Boiler control objects with strong coupling, large delay, large inertia characteristics.Fuzzy control on object for low, fast response and predict advanced features. Design the fuzzy predictive controller for chain coalfired hot blast stove temperature control.The test results show that the fuzzy predictive control system is improved effectively the static precision and dynamic characteristic.Fuzzy predictive control for large delay system has better practicability.

Neural Network Predictive Control Method of Sand Mixing Device

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Meiqiu Li ◽  
Yuanhua Zhou ◽  
Ye Tian ◽  
Bangxiong Wu
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Method ◽  
Neural Network Predictive Control

PID Parameter Setting Based on Fuzzy Control Algorithm and its Application in the Temperature Control System of Airplane

2012 ◽  
Vol 588-589 ◽  
pp. 1503-1506
Author(s):  
Fang Ding ◽  
Tao Ma
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Control Algorithm ◽  
Control Method ◽  
Fuzzy Controller ◽  
Temperature Control System ◽  
System Control ◽  
Control Effect ◽  
Fuzzy Control Algorithm

This Temperature control system of aircraft cabin is a complex system with nonlinear, time-varying, model inaccurate and work environment uncertain. According to the system control requirements, the fuzzy controller with the characteristic of fast response speed, good stability and strong resistance to interference is used in the study. The system error is adjusted constantly by using fuzzy control algorithm and simulation study is conducted in the software Matlab. The results are showed that control effect of control method used in this study is better than the traditional PID control method, and dynamic performance, steady state accuracy and robustness of system is effectively improved.

Design of Temperature Control System Based on DSP and Fuzzy Neural Network

2013 ◽  
Vol 823 ◽  
pp. 384-387
Author(s):  
Ya Juan Chen ◽  
Yue Hong Zhang ◽  
Gen Wang Ying
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Good Control ◽  
Temperature Control System ◽  
Control Effect ◽  
Fuzzy Neural ◽  
Electric Boiler ◽  
Self Tuning

Using fuzzy neural network to tune PID parameters, and DSP as processor, it was designed that a set of electric boiler temperature control system based on PID parameters self-tuning, including the design of each hardware module and each software subroutine of the system. Experimental results show that compared with the traditional PID temperature control system, this temperature control system has the advantages such as good control effect, easy parameter adjustment, strong anti-jamming capability, better adaptability and robustness, has the feasibility and practical value.

Research on tool control system of double cutters experimental platform based on fuzzy neural network predictive control

2021 ◽  
Vol 40 (1) ◽  
pp. 65-76
Author(s):  
Peng Zhou ◽  
Junxing Tian ◽  
Jian Sun ◽  
Jinmei Yao ◽  
Defang Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Fuzzy Controller ◽  
Control Signal ◽  
Control Technology ◽  
Fuzzy Neural ◽  
Neural Network Predictive Control

According to the characteristics of the tool hydraulic control system of the double cutters experimental pplatform, intelligent control methodology forecasted by fuzzy neural network is introduced into the control system. The two level control systems of fuzzy neural network predictive control and fuzzy control are designed. The fuzzy neural network predictive controller mainly completes the analysis and control of the speed and pressure in the tool hydraulic system. The speed control signal and pressure control signal from the first level are output to the fuzzy controller. Then, through logical reasoning, the control signal is output and the actuator is driven by the fuzzy controller to complete the control function of the tool system. In this paper, compared with the traditional PID control, the fuzzy neural network predictive control technology has better control accuracy, dynamic response performance and steady-state accuracy. The fuzzy neural network predictive control technology can be used to control the tool hydraulic system of Tunnel Boring Machine.

Plastic Tank Temperature Control System Based on OPC Technology

2014 ◽  
Vol 701-702 ◽  
pp. 812-815
Author(s):  
Lu Wang ◽  
You Zhi Ren ◽  
Hui Liu ◽  
Ya Wei Yang ◽  
Qian Zeng
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Pid Control ◽  
Control Method ◽  
Temperature Control System ◽  
Control Effect ◽  
Plc Control ◽  
Opc Technology ◽  
Neural Network Pid ◽  
Better Than

This paper analyzes the characteristics of the PLC and MATLAB, describes the OPC technology and introduces the system structures and procedures which uses the OPC technology to achieve the exchange of data between PLC and MATLAB system. For example, in the plastic tank temperature control system, it mentions the BP neural network PID control algorithm which is applied to the PLC control system by using the MATLAB intelligent controller. The results show that this control method is simple and the control effect is good, much better than the traditional PID control.

The Application of PID Based on Optimized RBF in Thickness Control of Strip Steel

2012 ◽  
Vol 466-467 ◽  
pp. 52-56
Author(s):  
Yu Zhen Yu ◽  
Xin Yi Ren ◽  
Chun Yan Deng ◽  
Xiao Hui Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Strategy ◽  
Pid Controller ◽  
Control Method ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Strip Thickness ◽  
Control Effect ◽  
Thickness Control

The strip thickness control system is difficult to establish an accurate mathematical model, and traditional PID control strategy has a poor adaptive ability, so the effect of control is always not satisfying. According to the problems above, a new control strategy of self-tuning PID controller based on RBF neural network whose parameters are optimized by PSO algorithm is proposed in the paper. The control method integrates advantages of RBF neural network as well as PID controller and good global search capability of PSO algorithm. The simulation results indicate that the method not only improves control performance and dynamic quality, but also has strong self-adapting ability and robustness. It achieved a very good control effect when used in strip thickness control system that proved the correctness and effectiveness of the control method.

Design of a Temperature Control System Based on Generalized Predictive Control

2015 ◽  
Vol 713-715 ◽  
pp. 897-900
Author(s):  
Yun Feng Peng ◽  
Chang Shu ◽  
Xiang Lin Tan ◽  
Zhi Chao Shao
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Temperature Control ◽  
Pid Control ◽  
Control Method ◽  
Inertial Navigation ◽  
Temperature Control System ◽  
Generalized Predictive Control ◽  
Matlab Simulation

According to the unsatisfied control result in a military inertial navigation platform,a control method combined with generalized predictive control and PID control is proposed,accurate control can be realized in terms of time and temperature. MATLAB simulation is given to illustrate that this method satisfies expected requirements well,and is feasible and available.

The Application of Neural Network in Nonlinear System

2011 ◽  
Vol 179-180 ◽  
pp. 128-134
Author(s):  
Lei Shi ◽  
Xing Cheng Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Nonlinear System ◽  
Predictive Control ◽  
Network Theory ◽  
Neural Network Theory ◽  
Nerual Network ◽  
Neural Network Predictive Control

Neural network theory is widely applied to predictive control system because of its superiority in dealing with nonlinearities therein. Meanwhile, various algorithms for neural network predictive control have been put forward..The paper investigates the application of neural network-based control in nonlinear system. Especially, some current important nerual network-based controls are remarked and the developments are prospected.

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