Document Type
Journal Article
Publication Title
Electronics
Volume
10
Issue
11
Publisher
MDPI
School
School of Engineering
RAS ID
36664
Funders
Edith Cowan University
Abstract
Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control.
DOI
10.3390/electronics10111245
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Alwan, N. A. S., & Hussain, Z. M. (2021). Deep learning control for digital feedback systems: Improved performance with robustness against parameter change. Electronics, 10(11), article 1245. https://doi.org/10.3390/electronics10111245