Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change

Version 1 : Received: 23 April 2021 / Approved: 26 April 2021 / Online: 26 April 2021 (12:16:18 CEST)

How to cite: Alwan, N.A.S.; Hussain, Z.M. Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change. Preprints 2021, 2021040664 (doi: 10.20944/preprints202104.0664.v1). Alwan, N.A.S.; Hussain, Z.M. Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change. Preprints 2021, 2021040664 (doi: 10.20944/preprints202104.0664.v1).

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, like sensor networks.

Subject Areas

deep learning; feedback control; conventional controller; neural network; backpropagation.

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