Privacy-Preserving Platooning Control of Vehicular Cyber–Physical Systems With Saturated Inputs Article Swipe
Dengfeng Pan
,
Derui Ding
,
Xiaohua Ge
,
Qing‐Long Han
,
Xian‐Ming Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tsmc.2022.3226901
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tsmc.2022.3226901
Metaverse allows the physical reality to tightly integrate with the digital universe. As one typical metaverse application, platooning control of vehicular cyber–physical systems has attracted extensive attention as it is beneficial to improve traffic efficiency, driving safety, and emission reduction. However, due to the open nature of wireless communication networks, the transmitted vehicle-to-vehicle (V2V) data packets become exposed to the public and concomitant data leakage can lead to unintended consequences to vehicular platoons. This article is concerned with the privacy-preserving platooning control issue of vehicular cyber–physical systems with input saturations. First, a novel distributed proportional-integral observer is proposed to estimate the full state of each vehicle, where the integral terms with a forgetting factor facilitate to realize the tradeoff between transient performance and steady-state performance for the platoon. Second, sampled-data-based dynamic encryption and decryption schemes, featuring a dynamic private key, are developed such that the encrypted and decrypted V2V data can be kept private to each platoon vehicle. It is then shown that the platooning control problem over a generic communication topology can be cast into the stability issue of an auxiliary dynamic system. Furthermore, sufficient conditions on the existence of the desired observer and controller gains as well as the private key parameter selection are derived to guarantee the desired platoon stability and privacy preservation requirements. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed control method.
Related Topics
Concepts
Platoon
Cyber-physical system
Computer science
Encryption
Network packet
Key (lock)
Wireless
Transient (computer programming)
Observer (physics)
Computer security
Computer network
Control engineering
Control (management)
Engineering
Telecommunications
Artificial intelligence
Operating system
Quantum mechanics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tsmc.2022.3226901
- https://ieeexplore.ieee.org/ielx7/6221021/10073973/09991185.pdf
- OA Status
- hybrid
- Cited By
- 38
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312560475
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312560475Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tsmc.2022.3226901Digital Object Identifier
- Title
-
Privacy-Preserving Platooning Control of Vehicular Cyber–Physical Systems With Saturated InputsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-19Full publication date if available
- Authors
-
Dengfeng Pan, Derui Ding, Xiaohua Ge, Qing‐Long Han, Xian‐Ming ZhangList of authors in order
- Landing page
-
https://doi.org/10.1109/tsmc.2022.3226901Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6221021/10073973/09991185.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6221021/10073973/09991185.pdfDirect OA link when available
- Concepts
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Platoon, Cyber-physical system, Computer science, Encryption, Network packet, Key (lock), Wireless, Transient (computer programming), Observer (physics), Computer security, Computer network, Control engineering, Control (management), Engineering, Telecommunications, Artificial intelligence, Operating system, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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38Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 18, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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