Cyber-security in networked and distributed model predictive control. (2022)

• Record Type: Journal Article
• Authors: Arauz, T.; Chanfreut, P.; Maestre, J.M.
• Is Part Of: Annual reviews in control. Volume 53(2022)
• Page Start: 338
• Page End: 355
• Publication Date: 2022
• Subjects: Cyber-security -- Distributed control -- Model predictive control -- Learning; Automatic control -- Periodicals; Periodicals; 629.805
• DOI: 10.1016/j.arcontrol.2021.10.005
• Languages: English
• ISSNs: 1367-5788

Abstract

Abstract: Distributed model predictive control (DMPC) schemes have become a popular choice for networked control problems. Under this approach, local controllers use a model to predict its subsystem behavior during a certain horizon so as to find the sequence of inputs that optimizes its evolution according to a given criterion. Some convenient features of this method are the explicit handling of constraints and the exchange of information between controllers to coordinate their actuation and minimize undesired mutual interactions. However, we find that schemes have been developed naively, presenting flaws and vulnerabilities that malicious entities can exploit to gain leverage in cyber-attacks. The goal of this work is to raise awareness about this issue by reviewing the vulnerabilities of DMPC methods and surveying defense mechanisms. Finally, several examples are given to indicate how these defense mechanisms can be implemented in DMPC controllers.