scholarly journals Friction parameters identification and compensation of LuGre model base on genetic algorithms

2015 ◽  
Author(s):  
Yuqin Wen ◽  
Ming Chu ◽  
Hanxu Sun
Keyword(s):  
Genetic Algorithms ◽  
Parameters Identification ◽  
Lugre Model ◽  
Model Base ◽  
Friction Parameters

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

The Application of Hybrid Genetic Algorithms Model in Solving Product Optimization Problem

2013 ◽  
Vol 760-762 ◽  
pp. 2146-2150
Author(s):  
Xiu Feng Shao ◽  
Jian Zhang ◽  
Li Li
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problem ◽  
Real Data ◽  
Basic Theory ◽  
Model Base ◽  
Hybrid Genetic Algorithms ◽  
Product Optimization

We introduce basic theory of genetic algorithms in this paper, Build one hybrid genetic algorithms model base on genetic algorithms in solving product optimization problem. And we realize the model by software. The disposal result of real data tells us that the model has good practicability and accuracy to the problem of product optimization problem.

Parameter Identification of LuGre Friction Model for Robot Joints

2012 ◽  
Vol 479-481 ◽  
pp. 1084-1090 ◽  
Author(s):  
Ya Qing Zheng
Keyword(s):  
Theoretical Foundation ◽  
Friction Model ◽  
Parameters Identification ◽  
Dynamic Parameters ◽  
Lugre Model ◽  
Friction Behavior ◽  
Research Issues ◽  
Lugre Friction Model ◽  
Lugre Friction ◽  
Experimental Validations

The LuGre friction model well captures most of the friction behavior, but it was very difficult to identify the parameters of the LuGre model. The LuGre friction model, theory of static and dynamic parameters identification of the LuGre model as well as the algorithm based on particle swarm optimization are summarized according to the previous work. Then the programs for the static and dynamic parameters identification are made and analyzed in the environment of Matlab software in detail, and the identification results are given. The work mentioned above will lay the theoretical foundation for the future experimental validations and provide the detailed models, algorithms and programs for the corresponding research issues.

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