Predictive control and communication co-design: a Gaussian process regression approach

Girgis, Abanoub M, Park, Jihong, Liu, Chen-Feng and Bennis, Mehdi 2020, Predictive control and communication co-design: a Gaussian process regression approach, in SPAWC 2020 : Proceedings of the 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, Institute of Electrical and Electronics Engineers, Piscataway, N.J., pp. 1-5, doi: 10.1109/SPAWC48557.2020.9154304.

Attached Files
Name Description MIMEType Size Downloads

Title Predictive control and communication co-design: a Gaussian process regression approach
Author(s) Girgis, Abanoub M
Park, JihongORCID iD for Park, Jihong orcid.org/0000-0001-7623-6552
Liu, Chen-Feng
Bennis, Mehdi
Conference name IEEE Signal Processing Society. Workshop (21st : 2020 : Atlanta, Georgia/Online)
Conference location Atlanta, Georgia/Online
Conference dates 2020/05/26 - 2020/05/29
Title of proceedings SPAWC 2020 : Proceedings of the 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications
Editor(s) [Unknown]
Publication date 2020
Series IEEE Signal Processing Society Workshop
Start page 1
End page 5
Total pages 5
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Keyword(s) 6G
predictive control
age of information
communication and control co-design
Gaussian process regression
Summary While remote control over wireless connections is a key enabler for scalable control systems consisting of multiple actuator-sensor pairs, i.e., control systems, it entails two technical challenges. Due to the lack of wireless resources, only a limited number of control systems can be served, making the state observations outdated. Further, even after scheduling, the state observations received through wireless channels are distorted, hampering control stability. To address these issues, in this article we propose a scheduling algorithm that reduces the age-of-information (AoI) of the last received states. Meanwhile, for non-scheduled sensor-actuator pairs, we propose a machine learning (ML) aided predictive control algorithm, in which states are predicted using a Gaussian process regression (GPR). Since the GPR prediction credibility decreases with the AoI of the input data, both predictive control and AoI-based scheduler should be co-designed. Hence, we formulate a joint scheduling and transmission power optimization via the Lyapunov optimization framework. Numerical simulations corroborate that the proposed co-designed predictive control and AoI based scheduling achieves lower control errors, compared to a benchmark scheme using a round-robin scheduler without state prediction.
ISBN 9781728154787
Language eng
DOI 10.1109/SPAWC48557.2020.9154304
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141931

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 18 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Tue, 15 Sep 2020, 13:25:06 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.