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

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.

ChemInform Abstract: Thermal Stress Management of a Solid Oxide Fuel Cell Using Neural Network Predictive Control

ChemInform ◽  
2014 ◽  
Vol 45 (30) ◽  
pp. no-no
Author(s):  
S. A. Hajimolana ◽  
S. M. Tonekabonimoghadam ◽  
M. A. Hussain ◽  
M. H. Chakrabarti ◽  
N. S. Jayakumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Stress ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Stress Management ◽  
Predictive Control ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Neural Network Predictive Control

NEURAL NETWORK PREDICTIVE CONTROL IN A NATURALLY VENTILATED AND FOG COOLED GREENHOUSE

2012 ◽  
pp. 45-52 ◽  
Author(s):  
E. Fitz-Rodríguez ◽  
M. Kacira ◽  
F. Villarreal-Guerrero ◽  
G.A. Giacomelli ◽  
R. Linker ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Neural Network Predictive Control

DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Mohd Shahrieel Mohd Aras ◽  
Shahrum Shah Abdullah ◽  
Ahmad Fadzli Nizam Abdul Rahman ◽  
Norhaslinda Hasim ◽  
Fadilah Abdul Azis ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Fuzzy Logic Controller ◽  
Pi Controller ◽  
System Response ◽  
Linear Quadratic ◽  
Remotely Operated Vehicle ◽  
Depth Control ◽  
Linear Quadratic Regulation ◽  
Neural Network Predictive Control

This paper investigates the depth control of an unmanned underwater remotely operated vehicle (ROV) using neural network predictive control (NNPC). The NNPC is applied to control the depth of the ROV to improve the performances of system response in terms of overshoot. To assess the viability of the method, the system was simulated using MATLAB/Simulink by neural network predictive control toolbox. In this paper also investigates the number of data samples (1000, 5000 and 10,000) to train neural network. The simulation reveals that the NNPC has the better performance in terms of its response, but the execution time will be increased. The comparison between other controller such as conventional PI controller, Linear Quadratic Regulation (LQR) and fuzzy logic controller also covered in this paper where the main advantage of NNPC is the fastest system response on depth control. 

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