Application of a neural network predictive control based on GGAP-RBF for the supercritical main steam

2012 ◽  
Author(s):  
Yun-Juan Li ◽  
Yan-jun Fang ◽  
Qi Li
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Main Steam ◽  
Neural Network Predictive Control

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. 

scholarly journals A Mental Simulation Approach for Learning Neural-Network Predictive Control (in Self-Driving Cars)

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 192041-192064
Author(s):  
Mauro Da Lio ◽  
Riccardo Dona ◽  
Gastone Pietro Rosati Papini ◽  
Francesco Biral ◽  
Henrik Svensson
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Mental Simulation ◽  
Simulation Approach ◽  
Self Driving Cars ◽  
Neural Network Predictive Control

Neural network predictive control of vibrations in tall structure: An experimental controlled vision

2021 ◽  
Vol 89 ◽  
pp. 106940
Author(s):  
Mohsin Jamil ◽  
Muhammad Nasir Khan ◽  
Saqib Jamshed Rind ◽  
Qasim Awais ◽  
Muhammad Uzair
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Tall Structure ◽  
Neural Network Predictive Control
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