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

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.

Automated summary

This paper presents an adaptive speed controller for electrical drives based on the Recurrent Wavelet Neural Network (RWNN). Oscillation damping and robustness against parameter changes are achieved using online network parameters updates. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. The effectiveness of the method is validated through numerical tests and experimental results.