Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles

Hong Mo; Yinghui Meng; Fei-Yue Wang; Dongrui Wu

doi:10.1109/JAS.2022.105806

Volume 9 Issue 9

Sep. 2022

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 11.8, Top 4% (SCI Q1)

CiteScore: 17.6, Top 3% (Q1)
Google Scholar h5-index: 77， TOP 5

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(9): 1658-1672

H. Mo, Y. H. Meng, F.-Y. Wang, and D. R. Wu, “Interval type-2 fuzzy hierarchical adaptive cruise following-control for intelligent vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1658–1672, Sept. 2022. doi: 10.1109/JAS.2022.105806

Citation:

H. Mo, Y. H. Meng, F.-Y. Wang, and D. R. Wu, “Interval type-2 fuzzy hierarchical adaptive cruise following-control for intelligent vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1658–1672, Sept. 2022. doi: 10.1109/JAS.2022.105806

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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles

doi: 10.1109/JAS.2022.105806

Hong Mo^{1
,
,},
Yinghui Meng^1
,,
Fei-Yue Wang^{2, 3, 4
,},
Dongrui Wu^5
,

1.
College of Electric and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China
2.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
3.
Qingdao Academy of Intelligent Industries, Qingda 266109
4.
Center of China Economic and Social Security, University of Chinese Academy of Sciences, Beijing 100190, China
5.
Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control, the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: This work was partially supported by the National Natural Science Foundation of China (61473048, 61074093, 61873321)

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Author Bio:
Hong Mo received the M.S. degree in fundamental mathematics from Guangxi University in 2001, and the Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences, in 2004. She is currently a Professor with College of Electrical and Information Engineering, Changsha University of Science and Technology. Her research interests include fuzzy artificial intelligence, management and control of complex systems, and intelligent medical. She has authored over 80 papers published in academic journals and conferences and her two academic monographes were published in 2013 and 2018, respectively. She was selected into the Young Talent Program of Changsha University of Science and Technology in 2011, and selected as the Young Backbone Teachers of Hunan Province in 2012, the Excellent Master’s Thesis Supervisor of Hunan Province in 2018, also received the Hunan Natural Science Award in 2013, Nature Science Award of Chinese Automation Association in 2016, the 8th ABB Cup National Automation System Engineering Thesis Competition Award, the Best Representation Award of 2018 International Conference on Fuzzy Theory and Its Application in 2018, Her team won the Third XiangNeng Chutian Electric Power Special Scholarship in 2019. Currently, Prof. Mo is the Associate Editor of International Journal of Fuzzy Systems, and also Associate Editor of Acta Automatica Sinica and Chinese Journal of Intelligent Science and Technology. She is the Vice Director of the CAA (Chinese Association of Automation) Technical Committee of Granular Computing and Multi-scale Analysis, and the Member of IEEE ITSS Social Transportation Systems Technical Committee, and also the Member of the CAA (Chinese Association of Automation) Technical Committee of parallel Intelligence, the Member of the CSF (China Simulation Federation) Technical Committee of Artificial Society

Yinghui Meng received the B.E. degrees in automation from Zhongyuan University of Technology in 2019. He is currently a master student in control science and engineering at the College of Electrical and Information Engineering, Changsha University of Science and Technology.His research interests include type-2 fuzzy sets, type-2 fuzzy control and intelligent driving

Fei-Yue Wang (Fellow, IEEE) received the Ph.D. degree in computer and systems engineering from the Rensselaer Polytechnic Institute, USA in 1990. He joined The University of Arizona in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), China, under the support of the Outstanding Chinese Talents Program from the State Planning Council, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS, and Vice President of Institute of Automation, CAS in 2006. He found CAS Center for Social Computing and Parallel Management in 2008, and became the State Specially Appointed Expert and the Founding Director of the State Key Laboratory for Management and Control of Complex Systems in 2011. His current research focuses on methods and applications for parallel intelligence, social computing, and knowledge automation. He is a Fellow of INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China, numerous best papers awards from IEEE Transactions, and became an Outstanding Scientist of ACM for his work in intelligent control and social computing. He received the IEEE ITS Outstanding Application and Research Awards in 2009, 2011, and 2015, respectively, the IEEE SMC Norbert Wiener Award in 2014, and became the IFAC Pavel J. Nowacki Distinguished Lecturer in 2021. Since 1997, he has been serving as the General or Program Chair of over 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of the IEEE ITS Society from 2005 to 2007, the IEEE Council of RFID from 2019 to 2021, the Chinese Association for Science and Technology, USA in 2005, the American Zhu Kezhen Education Foundation from 2007 to 2008, the Vice President of the ACM China Council from 2010 to 2011, the Vice President and the Secretary General of the Chinese Association of Automation from 2008 to 2018, the Vice President of IEEE Systems, Man, and Cybernetics Society from 2019 to 2021. He was the Founding Editor-in-Chief (EiC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, IEEE ITS Magazine from 2006 to 2007, IEEE/CAA Jorhao of Automatica Sinica from 2014–2017, China’s Journal of Command and Control from 2015–2021, and China’s Journal of Intelligent Science and Technology from 2019 to 2021. He was the EiC of the IEEE Intelligent Systems from 2009 to 2012, IEEE Transactions on Intelligent Transportation Systems from 2009 to 2016, IEEE Transactions on Computational Social Systems from 2017 to 2020. Currently, he is the President of CAA’s Supervision Council, and the EiC of IEEE Transactions on Intelligent Vehicles

Dongrui Wu received the B.E degree in automatic control from the University of Science and Technology of China in 2003, the M.Eng degree in electrical and computer engineering from the National University of Singapore in 2006, and the Ph.D. degree in electrical engineering from the University of Southern California, USA in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interests include brain-computer interfaces, machine learning, computational intelligence, and affective computing. He has more than 180 publications (8600+ Google Scholar citations; h = 47). He received the IEEE Computational Intelligence Society (CIS) Outstanding Ph.D. Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the North American Fuzzy Information Processing Society (NAFIPS) Early Career Award in 2014, the IEEE Systems, Man and Cybernetics (SMC) Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, and the Chinese Association of Automation Early Career Award in 2021. His team won the First Prize of the China Brain-Computer Interface Competition in three successive years (2019−2021).Prof. Wu is Associate Vice President for Human-Machine Systems of the IEEE SMC Society and the Editor-in-Chief of its eNewsLetter. He is/was an Associate Editor of IEEE Transactions on Fuzzy Systems (2011−2018; 2020−2021), IEEE Transactions on Human-Machine Systems (2014−2021), IEEE Computational Intelligence Magazine (since 2017), and IEEE Transactions on Neural Systems and Rehabilitation Engineering (since 2019)
Corresponding author: Hong Mo, e-mail: mohong198@163.com
Received Date: 2022-01-24
Accepted Date: 2022-02-16

Available Online: 2022-08-03

Abstract

Abstract

Intelligent vehicles can effectively improve traffic congestion and road traffic safety. Adaptive cruise following-control (ACFC) is a vital part of intelligent vehicles. In this paper, a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model, type-2 fuzzy control, feedforward + fuzzy proportion integration (PI) feedback (F+FPIF) control, and inverse longitudinal dynamics model of vehicles. Firstly, a traditional variable time headway model is improved considering the acceleration of the lead car. Secondly, an interval type-2 fuzzy logic controller (IT2 FLC) is designed for the upper structure of the ACFC system to simulate the driver’s operating habits. To reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration, the control strategy of F+FPIF is given for the lower control structure. Thirdly, the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method (no lower controller for tracking desired acceleration) separately. Meanwhile, the proportion integration differentiation (PID), linear quadratic regulator (LQR), subsection function control (SFC) and type-1 fuzzy logic control (T1 FLC) are respectively compared with the IT2 FLC in control performance under different scenes. Finally, the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

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References(95)

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沈阳化工大学材料科学与工程学院沈阳 110142

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The traditional variable time headway model is improved by considering the influence of acceleration of lead car on car-following safety distance, and used as the safety distance model of the vehicle following system. Meanwhile, its stability is proved
An interval type-2 fuzzy logic controller (IT2 FLC), which has no relation to the parameters of vehicle model, is designed for the upper control structure to simulate the driver's operating habits. It takes relative speed and distance difference as the input variables, the desired acceleration as the output variable
"Feedforward + fuzzy PI feedback" control is utilized for the lower structure to improve the tracking speed and accuracy of the desired acceleration

Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles

doi: 10.1109/JAS.2022.105806

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通讯作者: 陈斌, bchen63@163.com

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