Optimal Motion Generation of a Flexible Macro-micro Manipulator System Using Genetic Algorithm and Neural Network

2006 ◽  
Author(s):  
Yu Zhang ◽  
Zengqi Sun ◽  
Tangwen Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Motion Generation ◽  
Optimal Motion ◽  
Micro Manipulator

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

scholarly journals Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

2019 ◽  
Vol 38 ◽  
pp. 117-124
Author(s):  
Guang Hu ◽  
Zhi Cao ◽  
Michael Hopkins ◽  
Conor Hayes ◽  
Mark Daly ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The Neural Network
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