Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

被引：15
|
作者：
Zychlewicz, Mateusz [1 ]
Stanislawski, Radoslaw [1 ]
Kaminski, Marcin [1 ]
机构：
[1] Wroclaw Univ Sci & Technol, Dept Elect Machines Drives & Measurements, Fac Elect Engn, 19 Smoluchowskiego St, PL-50372 Wroclaw, Poland
来源：
ELECTRONICS | 2022年 / 11卷 / 02期
关键词：
wavelet neural network; grey wolf optimizer; adaptive speed control; design process optimization; two-mass drive; NETWORK; MOTOR; PERFORMANCE; ALGORITHM; DRIVE;
D O I：
10.3390/electronics11020177
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally.
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页数：23
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