Neural Adaptive Sliding-Mode Control of Vehicular Cyber-Physical Systems With Uniformly Quantized Communication Data and Disturbances

Yuan Zhao; Mengchao Li; Zhongchang Liu; Lichuan Liu; Shixi Wen; Lei Ding

doi:10.1109/JAS.2025.125186

IEEE/CAA Journal of Automatica Sinica

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Y. Zhao, M. Li, Z. Liu, L. Liu, S. Wen, and L. Ding, “Neural adaptive sliding-mode control of vehicular cyber-physical systems with uniformly quantized communication data and disturbances,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125186

Citation:

Y. Zhao, M. Li, Z. Liu, L. Liu, S. Wen, and L. Ding, “Neural adaptive sliding-mode control of vehicular cyber-physical systems with uniformly quantized communication data and disturbances,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125186

Citation:

Y. Zhao, M. Li, Z. Liu, L. Liu, S. Wen, and L. Ding, “Neural adaptive sliding-mode control of vehicular cyber-physical systems with uniformly quantized communication data and disturbances,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125186

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Neural Adaptive Sliding-Mode Control of Vehicular Cyber-Physical Systems With Uniformly Quantized Communication Data and Disturbances

doi: 10.1109/JAS.2025.125186

Funds: This work was supported by the National Natural Science Foundation of China (62173079, 62473203), Liaoning Provincial Science and Technology Plan Joint Program (2024-MSLH-019), the Education Department of Liaoning Province (LJKMZ20221840), Interdisciplinary project of Dalian University (DLUXK-2024-YB-004)

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Author Bio:
Yuan Zhao received the B.S. degree in automation, and the Ph.D. degree in control theory and control engineering from Dalian Maritime University, in 2010 and 2016, respectively. From 2016 to 2022, she was a Lecturer with Tianjin Normal University. From 2018 to 2019, she also worked as a Research Associate at The Chinese University of Hong Kong, Hong Kong SAR, China. Now She joints Dalian University. Her research interests include connected automated vehicles, cooperative control and security in intelligent transportation systems

Mengchao Li received the B.S. degree in automation from Dalian University of China in 2022. She is currently a master student in control theory and control engineering at Dalian University. Her research interests include adaptive control of connected automated vehicles, and security in intelligent transportation systems

Zhongchang Liu (Mermber, IEEE) received the B.S. degree and the M.S. degree from Dalian Maritime University, in 2009 and 2011, respectively, and the Ph.D. degree in information engineering from The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China in 2015. He was a Visiting Fellow in Boston University, USA in the summer of 2013. From 2015 to 2016, he worked as a Postdoctoral Fellow with CUHK. From 2016 to 2017, he was a Research Associate with Sun Yat-sen University. Then, he joined Dalian Maritime University, where he is currently an Associate Professor with the College of Marine Electrical Engineering. His research interests include cooperative control of distributed systems, networked control and synchronization problems in multi-agent systems

Lichuan Liu received the B.S. degree in automation and the M.E. degree in control science and control engineering from Dalian Maritime University, in 2010 and 2012, respectively. He is currently an Senior Laboratory Engineer at Dalian Maritime University. His research interests include robot motion control and embedded system development

Shixi Wen (Senior Member, IEEE) received the M.E. and Ph.D. degrees in control science and control engineering from Dalian Maritime University, in 2011 and 2015, respectively. From 2015 to 2017, he was a Post-Doctoral Research Fellow at Dalian University of Technology. He is currently an Associate Professor at Dalian University. His research interests include networked control systems and vehicular cooperative control in intelligent vehicle highway systems

Lei Ding ((Senior Member, IEEE)) received the B.Eng. degree in automation, and the M.Eng. and Ph.D. degrees in control theory and control engineering from the Dalian Maritime University, in 2007, 2009, and 2014, respectively. From 2015 to 2016, he was a Visiting Research Fellow with Western Sydney University, Australia. From 2016 to 2017, he was a Postdoctoral Associate with Wayne State University, USA. From 2017 to 2019, he worked as a Research Fellow with the Swinburne University of Technology, Australia. He is currently a Full Professor at the Nanjing University of Posts and Telecommunications. His research interests include cooperative control of networked systems and their applications to smart grids. Professor Ding was the recipient of the 2021 IEEE/CAA Journal of Automatica Sinica Norbert Wiener Review Award, the 2020 IEEE Transactions on Industrial Informatics Outstanding Paper Award, and the 2019 IEEE Systems, Man, and Cybernetics (SMC) Society Andrew P. Sage Best Transactions Paper Award (IEEE Transactions on Cybernetics). He received the 2018 IEEE Transactions on Cybernetics Outstanding Reviewer. Hr is an Associate Editor of IEEE Transactions on Industrial Informatics and IEEE/ CAA Journal of Automatica. He was a Guest Editor of Control Engineering Practice, IEEE Transactions on Systems, Man, and Cybernetics: Systems and Information Sciences; the Chair of IEEE IES Technical Committee on Network-Based Control Systems and Applications; the Technical Track Co-chair for several international conferences
Corresponding author: Lei Ding, e-mail: dinglei@njupt.edu.cn
¹ Dynamometer Drive Schedules: https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules.
Received Date: 2024-07-19
Revised Date: 2024-12-13
Accepted Date: 2025-01-10

Available Online: 2025-03-05

Abstract

Abstract

This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems (VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data. To reduce the adverse effects of quantization errors on system performance, a coupling sliding mode surface is established for each following vehicle. The radial basis function (RBF) neural networks are employed to approximate the unknown external disturbances. Then, a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles. And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle’s controller parameters is independent of the entire communication topology. The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero. Compared with the existing controller design methods and quantization mechanisms, the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors, driving comfort and fuel economy. Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.
- Neural adaptive sliding-mode control,
- quantized communication,
- string stability,
- vehicular cyber-physical systems (VCPSs)

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¹ Dynamometer Drive Schedules: https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules.

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Neural Adaptive Sliding-Mode Control of Vehicular Cyber-Physical Systems With Uniformly Quantized Communication Data and Disturbances

doi: 10.1109/JAS.2025.125186

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