Semi-Active Control of Structures using a Combined Genetic Algorithm - Neural Network - Fuzzy Controller

2010 ◽  
Author(s):  
H. Ghaffarzadeh ◽  
V. Hamedi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Active Control ◽  
Fuzzy Controller

scholarly journals High-performance adaptive intelligent Direct Torque Control schemes for induction motor drives

2005 ◽  
Vol 2 (1) ◽  
pp. 93-116 ◽  
Author(s):  
M. Vasudevan ◽  
R. Arumugam ◽  
S. Paramasivam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Induction Motor ◽  
High Performance ◽  
Fuzzy Controller ◽  
Linear Time ◽  
Direct Torque Control ◽  
Torque Control ◽  
Advantages And Disadvantages ◽  
State Selection

This paper presents a detailed comparison between viable adaptive intelligent torque control strategies of induction motor, emphasizing advantages and disadvantages. The scope of this paper is to choose an adaptive intelligent controller for induction motor drive proposed for high performance applications. Induction motors are characterized by complex, highly non-linear, time varying dynamics, inaccessibility of some states and output for measurements and hence can be considered as a challenging engineering problem. The advent of torque and flux control techniques have partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Intelligent controllers are considered as potential candidates for such an application. In this paper, the performance of the various sensor less intelligent Direct Torque Control (DTC) techniques of Induction motor such as neural network, fuzzy and genetic algorithm based torque controllers are evaluated. Adaptive intelligent techniques are applied to achieve high performance decoupled flux and torque control. This paper contributes: i) Development of Neural network algorithm for state selection in DTC; ii) Development of new algorithm for state selection using Genetic algorithm principle; and iii) Development of Fuzzy based DTC. Simulations have been performed using the trained state selector neural network instead of conventional DTC and Fuzzy controller instead of conventional DTC controller. The results show agreement with those of the conventional DTC.

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.

Research of Genetic Algorithm Controller Based Fuzzy Neural Network in Vacuum Pre-Cooling Freshness-Keeping Machine

2014 ◽  
Vol 926-930 ◽  
pp. 3545-3549
Author(s):  
Ke Liang Zhou ◽  
Qiong Tan ◽  
Jian He
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Fuzzy Controller ◽  
Control Object ◽  
Bp Algorithm ◽  
Control Effect ◽  
Fuzzy Neural ◽  
The Difference

The control object is the temperature of pre-cooling machine, combined the advantage of neural network and genetic algorithm (GA). Adopting GA controller based fuzzy neural network. The controller doing the fuzzy reasoning to the difference of given temperature and sample temperature. GA does the offline training to the Connection weights and Membership function of fuzzy neural network, then uses BP algorithm to do further adjust online for parameters. Simulation result shows that the new controller achieves better control effect compared with traditional PID controller, fuzzy controller.

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.

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