Abstract
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.
Highlights
The reliability of the applied control methods is one of the main points of scientific work observed in research centers around the world
Among the techniques applied in this field, neural networks (NNs) are the fastest-growing group—they find use in robotics [1], optimization of complex control schemes, e.g., predictive control [2], or combinations with other intelligent structures such as fuzzy systems [3]
The real-life implementations include electrical drives. They are applied to control the speed of Permanent Magnet Synchronous Motors (PMSMs) [4], Induction Motors (IMs) [5], and systems with a complex mechanical part [6]
Summary
The reliability of the applied control methods is one of the main points of scientific work observed in research centers around the world. Through updating the weights online, adaptive structures for control solutions can be created [8] Another point considered during the application of a neural network is its topology. Translation and dilatation can be treated to weights in classical networks, so they can change their value in time to better adapt to the current conditions In control theory, this concept can be used to synthesize estimators [21], predictors [22], or controllers [23]. Unstable work (even over a short period) may lead to ruptures of the couplings and shafts connecting several parts of the system This issue was considered in this work, the GWO algorithm was used for the optimization of the starting point of the adaptive neural speed controller.
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