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

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.

Internet-Based Medical Service Use and Eudaimonic Well-Being of Urban Older Adults: A Peer Support and Technology Acceptance Model

“…According to the above literature review in Section 2.2, peer support will probably affect the well-being of older adults positively. Meanwhile, previous studies regarded digital feedback or intergenerational assistance as an important external variable for older adults to accept internet services [14,50,51]. Similarly, this study took peer support as an important independent variable and investigated whether psychological and technical guidance from peers can affect their behaviors and well-being in the process of their participation in IBMS.…”

Performance Analysis of Heat Exchanger System Using Deep Learning Controller

“…Controls like ANN and Fuzzy logic controls are becoming popular due to their model-free control architecture. [3][4] [5]. The closed-loop neural network control applications differ from open-loop classification and image processing applications.…”

“…As a result, deep learning has been used to solve a variety of pattern identification and classification issues. Two layer NN is sufficient for a feedback control system [4] but the addition of hidden layers improves the stability of the feedback control system and produces a damping effect [3]. However, to the authors' best knowledge, only few results have been published in the automatic control field.…”

“…However, to the authors' best knowledge, only few results have been published in the automatic control field. In a recent study [3], the deep learning control technique is used for the second order linear plant model and needs to extend for real non-linear systems. In [9] PID controller is replaced with a deep learning controller via a DBN for speed control of the DC motor.…”

“…The heat exchanger system, which is a non-linear system and needs adequate controller that can overcome the limitstions of PID controller and can also be used for non-linear systems. The Website: www.ijeer.forexjournal.co.in Performance Analysis of Heat Exchanger System Using Deep Learning Controller authors of a study [3] concluded that when subjected to a second-order linear plant in a closed-loop control system, their proposed deep learning controller worked best in the training phase as compared to works that use DBNs, RLs, and LSTMSNNs while keeping DL advantages. After an extensive survey of related literature, several lacune were discovered, one of them being the field of non-linear plant control problem, which very few studies have covered.…”