Active Control of Inflatable Structure Membrane Wrinkles Using Genetic Algorithm and Neural Network

2004 ◽  
Author(s):  
Fujun Peng ◽  
Yan-Ru Hu ◽  
Alfred Ng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Active Control ◽  
Inflatable Structure

The Analysis for Semi-Active Control Application in the MSCSS Subjected to Seismic Excitation

2013 ◽  
Vol 756-759 ◽  
pp. 254-256
Author(s):  
Hao Wu ◽  
Xun An Zhang ◽  
Ting Cai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Active Control ◽  
Seismic Wave ◽  
Fuzzy Neural Network ◽  
Passive Control ◽  
Time History ◽  
Seismic Excitation ◽  
Fuzzy Neural ◽  
Control Application

This Paper focuses on the semi-active control application in the Mega-Sub Controlled Structure System (MSCSS) subjected to seismic excitation. The semi-active control devices, which are installed in the MSCSS between the mega-structure and sub-structure, were designed by using fuzzy neural network, and those semi-active control rules were optimized to enhance the control efficiency by using the genetic algorithm. A semi-active control problem of the MSCSS subjected to seismic excitation was investigated, the time history analyses under different seismic excitation, which like El Centro seismic wave and Taft seismic wave, were performed by using MATLAB. The calculation results demonstrate that the semi-active control combining the fuzzy neural network and genetic algorithm can clearly enhances the seismic performance of the MSCSS and it also provides an improved reduction in the dynamic response when compared to the passive control scheme.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters
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