Parameter identification of Bouc-Wen model for MR fluid dampers using adaptive charged system search optimization

2012 ◽  
Vol 26 (8) ◽  
pp. 2523-2534 ◽  
Author(s):  
S. Talatahari ◽  
A. Kaveh ◽  
N. Mohajer Rahbari
Keyword(s):  
Parameter Identification ◽  
Mr Fluid ◽  
Charged System Search ◽  
Search Optimization ◽  
Charged System ◽  
Fluid Dampers

scholarly journals Parameter identification of Bouc-Wen model for Magnetorheological (MR) fluid Damper by a Novel Genetic Algorithm

2020 ◽  
Vol 12 (8) ◽  
pp. 168781402095054
Author(s):  
Birhan Abebaw Negash ◽  
Wonhee You ◽  
Jinho Lee ◽  
Kwansup Lee
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Mr Damper ◽  
Absolute Error ◽  
Mr Fluid ◽  
Fitness Evaluation ◽  
Damper Control ◽  
Fluid Dampers ◽  
Mr Fluid Damper ◽  
Crossover And Mutation

In this research, novel genetic algorithm (nGA) is proposed for Bouc-Wen modle parameters esstimation for magnetorheological (MR) fluid dampers. The optimization efficiency is improved by modifying the crossover and mutation steps of a GA. In the crossover stage, the probability of reproducing offspring from the same parent (same mother and father chromosome) is done to be zero, which may happen in the standard GA, and the probability of a chromosome to be selected for mating is based on error rank weighting of the chromosomes. Additional fitness evaluation of chromosomes will take place in between the crossover and mutation steps to save the best chromosome found so far, which is not implemented in the standard genetic algorithm (GA). The model is validated by comparing its simulation output force ( Fsim) with experimentally generated MR damper force ( Fexp). The mean absolute error, standard deviation and number of generations for convergence are taken as a criterias for performance evaluation. With these ctriterias, the proposed novel GA outperform better than the other researches. The accuracy is improved by 46.67% compared to standard GA. The proposed novel GA for Bouc-Wen model parameter identification can be used for any MR damper control system with better accuracy.

Development of an Optimization Framework for Parameter Identification and Shape Optimization Problems in Engineering

2011 ◽  
Vol 1 (1) ◽  
pp. 57-79 ◽  
Author(s):  
A. Andrade-Campos
Keyword(s):  
Inverse Problems ◽  
Shape Optimization ◽  
Parameter Identification ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimum Shape ◽  
Optimization Framework ◽  
Search Optimization ◽  
Initial Geometry

The use of optimization methods in engineering is increasing. Process and product optimization, inverse problems, shape optimization, and topology optimization are frequent problems both in industry and science communities. In this paper, an optimization framework for engineering inverse problems such as the parameter identification and the shape optimization problems is presented. It inherits the large experience gain in such problems by the SiDoLo code and adds the latest developments in direct search optimization algorithms. User subroutines in Sdl allow the program to be customized for particular applications. Several applications in parameter identification and shape optimization topics using Sdl Lab are presented. The use of commercial and non-commercial (in-house) Finite Element Method codes to evaluate the objective function can be achieved using the interfaces pre-developed in Sdl Lab. The shape optimization problem of the determination of the initial geometry of a blank on a deep drawing square cup problem is analysed and discussed. The main goal of this problem is to determine the optimum shape of the initial blank in order to save latter trimming operations and costs.

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