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Improved differential evolutionary algorithm for nonlinear identification of a novel vibration‐assisted swing cutting system
Author(s) -
Lu Mingming,
Wang Hao,
Zhao Dongpo,
Lin Jieqiong,
Gu Yan,
Yi Allen
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3008
Subject(s) - identification (biology) , differential evolution , parameter space , evolutionary algorithm , residual , algorithm , convergence (economics) , nonlinear system , computer science , swing , differential (mechanical device) , system identification , parameter identification problem , mathematical optimization , machining , control theory (sociology) , model parameter , mathematics , engineering , artificial intelligence , data modeling , control (management) , database , aerospace engineering , economic growth , biology , quantum mechanics , mechanical engineering , statistics , botany , physics , economics
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.