Improved differential evolutionary algorithm for nonlinear identification of a novel vibration‐assisted swing cutting system

Summary Vibration‐assisted swing cutting (VASC) is a new precision machining technology. VASC not only inherits the characteristics of EVC intermittent cutting but also alleviates the problem of EVC residual height. However, system identification is key if you want to achieve precise control. In ord...

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Vydáno v:International journal of adaptive control and signal processing Ročník 33; číslo 7; s. 1066 - 1078
Hlavní autoři: Lu, Mingming, Wang, Hao, Zhao, Dongpo, Lin, Jieqiong, Gu, Yan, Yi, Allen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.07.2019
Témata:
Cutting parameters
Evolutionary algorithms
Genetic algorithms
Hammerstein‐Wiener model
Identification
improved differential evolutionary algorithm
Mathematical models
Model accuracy
Optimization
Parameter identification
Precision machining
Solution strengthening
System identification
test functions
Vibration
vibration‐assisted swing cutting
ISSN:0890-6327, 1099-1115
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Shrnutí:Summary Vibration‐assisted swing cutting (VASC) is a new precision machining technology. VASC not only inherits the characteristics of EVC intermittent cutting but also alleviates the problem of EVC residual height. However, system identification is key if you want to achieve precise control. In order to solve this problem, a new improved differential evolutionary (IDE) algorithm is proposed to identify and optimize the Hammerstein‐Wiener model parameter in VASC system. IDE algorithm is applied to transform the identification problem of the model into the optimization problem in the parameter space, and the optimal solution of the parameter of the model in the parameter space is obtained. Meanwhile, the IDE algorithm and the conventional five differential evolutionary algorithms perform performance comparison tests on six different test functions. The test results show that the IDE algorithm is strengthening the global search capability, accelerate the convergence rate to the global optimal solution, and indicate that the IDE algorithm can be effectively applied to the parameter optimization of Hammerstein‐winner model. Based on the input and output data collected from the experiment, the accuracy of the identification model can be up to 98%, which prove the superiority of the proposed IDE algorithm for system identification.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3008