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
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 ◽  
...  

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

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Mohd Shahrieel Mohd Aras ◽  
Shahrum Shah Abdullah ◽  
Ahmad Fadzli Nizam Abdul Rahman ◽  
Norhaslinda Hasim ◽  
Fadilah Abdul Azis ◽  
...  

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. 


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 192041-192064
Author(s):  
Mauro Da Lio ◽  
Riccardo Dona ◽  
Gastone Pietro Rosati Papini ◽  
Francesco Biral ◽  
Henrik Svensson

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu

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.


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