Neural-Smith Predictor Method for Improvement of Networked Control Systems

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Haghniaz Jahromi Benyamin; Almodarresi Seyed MohammadTaghi; HAJEBI POOYA

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Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2021 | Volume:18 | Issue:1 (47) Page(s): 75-86

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Information Journal Paper

Title

Neural-Smith Predictor Method for Improvement of Networked Control Systems

Author(s)

Haghniaz Jahromi Benyamin | Almodarresi Seyed MohammadTaghi | HAJEBI POOYA | Issue Writer Certificate

Keywords

Networked Control Systems

Neural Networks

Neural-Smith predictor

Online System Modeling

Smith predictor

Stochastic Time Delay

Abstract

Networked Control Systems (NCSs) are distributed control systems in which the nodes, including controllers, sensors, actuators, and plants are connected by a digital communication network such as the Internet. One of the most critical challenges in Networked Control Systems is the Stochastic Time Delay of arriving data packets in the communication network among the nodes. Using the Smith predictor as the controller is a common solution to overcome network time delay. Online and accurate modeling of the plant improves the performance of the networked control system, especially when the plant is nonlinear and has unknown parameters and time-variant behavior. In this paper, a novel controller, Smith predictor/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">Neural-Smith predictor, is proposed, which firstly models plant using a perceptron neural network and secondly, another neural network is used as the core of signal processing of the controller. The parameters variation of the plant during time is considered online by the controller, and then the desired control signal is generated. The Integral of Time multiplied by the Absolut value of Error (ITAE) is a proper performance index for position control, so this index has been used to compare the results. Results of simulations show that NCS using the Smith predictor/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">Neural-Smith predictor has better performance in comparison to the common Smith predictor and the novel compensation method using a modified communication disturbance observer (MCDOB) when the values of network time delay and variation of plant’ s transfer function are excessive. For example, while the range of Stochastic Time Delay is between 19 and 21 ms, the difference between the ITAE of controllers is 0. 0004. This value increases to 0. 027, while the range of Stochastic Time Delay is between 910 and 930 ms.

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APA: Copy

Haghniaz Jahromi, Benyamin, Almodarresi, Seyed MohammadTaghi, & HAJEBI, POOYA. (2021). Neural-Smith Predictor Method for Improvement of Networked Control Systems. SIGNAL AND DATA PROCESSING, 18(1 (47) ), 75-86. SID. https://sid.ir/paper/956719/en

Vancouver: Copy

Haghniaz Jahromi Benyamin, Almodarresi Seyed MohammadTaghi, HAJEBI POOYA. Neural-Smith Predictor Method for Improvement of Networked Control Systems. SIGNAL AND DATA PROCESSING[Internet]. 2021;18(1 (47) ):75-86. Available from: https://sid.ir/paper/956719/en

IEEE: Copy

Benyamin Haghniaz Jahromi, Seyed MohammadTaghi Almodarresi, and POOYA HAJEBI, “Neural-Smith Predictor Method for Improvement of Networked Control Systems,” SIGNAL AND DATA PROCESSING, vol. 18, no. 1 (47) , pp. 75–86, 2021, [Online]. Available: https://sid.ir/paper/956719/en

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