Type-2 Fuzzy Control for Driving State and Behavioral Decisions of Unmanned Vehicle

Xuanming Zhao; Hong Mo; Kefu Yan; Lingxi Li

doi:10.1109/JAS.2019.1911810

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(1): 178-186

Xuanming Zhao, Hong Mo, Kefu Yan and Lingxi Li, "Type-2 Fuzzy Control for Driving State and Behavioral Decisions of Unmanned Vehicle," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 178-186, Jan. 2020. doi: 10.1109/JAS.2019.1911810

Citation:

Xuanming Zhao, Hong Mo, Kefu Yan and Lingxi Li, "Type-2 Fuzzy Control for Driving State and Behavioral Decisions of Unmanned Vehicle," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 178-186, Jan. 2020. doi: 10.1109/JAS.2019.1911810

Citation:

PDF( 1179 KB)

Type-2 Fuzzy Control for Driving State and Behavioral Decisions of Unmanned Vehicle

doi: 10.1109/JAS.2019.1911810

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

More Information

Author Bio:
Xuanming Zhao received the B. Eng. degree from Nankai University in 2016 and is currently working toward the master degree with the College of Electrical and Information Engineering, Changsha University of Science and Technology, China. His research interests include type-2 fuzzy sets, unmanned vehicle, and intelligent transportation systems

Hong Mo received the M.S. degree in fundamental mathematics from Guangxi University in 2001 and received 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 the College of Electric and Information Engineering, Changsha University of Science and Technology, China. Her research interests include type-2 fuzzy sets, linguistic dynamic systems, and fuzzy theory. She has authored over 60 papers published in academic journals and conferences and her two academic monographs were published in 2013 and 2018 respectively. 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 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 Technical Committee of parallel Intelligence

Kefu Yan received the B.Eng. degree from Nanjing Agricultural University, Nanjing, China, in 2015. He is currently working toward the M.S. degree at the College of Electrical and Information Engineering, Changsha University of Science and Technology, China. His current research interests include type-2 fuzzy sets, unmanned vehicle, and intelligent transportation systems

Lingxi Li (S’04−M’08−SM’14) received the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2008. Since August 2008, he has been with Indiana University-Purdue University Indianapolis (IUPUI) where he is currently Associate Professor in electrical and computer engineering. His research interests include modeling, analysis, and control of complex systems, intelligent transportation systems, intelligent vehicles, discrete event dynamic systems, and active safety systems. Dr. Li has served as Program Chair of 2011 and 2013 IEEE International Conference on Vehicular Electronics and Safety, and has been serving as Associate Editor for IEEE Transactions on Intelligent Transportation Systems since 2009. Dr. Li received Outstanding Editorial Service Award for IEEE Transactions on Intelligent Transportation Systems in 2012 and IUPUI Prestigious External Awards Recognition (PEAR) in 2013
Corresponding author: X. M. Zhao, H. Mo, and K. F. Yan are with the College of Electric and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China (e-mail: zhaoxxm@163.com; mohong198@163.com; 790927400@qq.com)
Received Date: 2019-07-19
Revised Date: 2019-08-21
Accepted Date: 2019-09-11

Available Online: 2019-10-23

Abstract

Abstract

In this paper, interval type-2 fuzzy sets, fuzzy comprehensive evaluation and the fuzzy control rules are synthesized to realize the control of unmanned vehicle in driving state and behavioral decisions. Compared to the type-1 fuzzy set, type-2 fuzzy sets have more advantages in handling the model based on uncertainties, linguistic information because the membership functions are fuzzy sets. Different membership functions are established for each factor when the unmanned vehicle is driving at different speed intervals. In addition, a new evaluation method is developed to analyze unmanned vehicle’s driving state. Finally, a set of dynamic fuzzy rules are sorted out, which can be applied to the unmanned vehicle’s behavioral decision-making and provide a new idea to related research.
- Fuzzy comprehensive evaluation,
- fuzzy control,
- interval type-2 fuzzy sets,
- unmanned vehicle

FullText(HTML)

References(29)

References

[1]	F.-Y. Wang, " Parallel control and management for intelligent transportation systems: concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630–638, Sep. 2010. doi: 10.1109/TITS.2010.2060218
[2]	F. H. Zhu, Z. J. Li, S. H. Chen, and G. Xiong, " Parallel transportation management and control system and its applications in building smart cities,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 6, pp. 1576–1585, Jun. 2016. doi: 10.1109/TITS.2015.2506156
[3]	F.-Y. Wang, N. N. Zheng, D. P. Cao, C. M. Martinez, L. Li, and T. Liu, " Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 577–587, Sep. 2017. doi: 10.1109/JAS.2017.7510598
[4]	T. X. Bai, S. Wang, Z. Shen, D. P. Cao, N. N. Zheng, and F.-Y. Wang, " Parallel robotics and parallel unmanned systems: framework, structure, process, platform and applications,” Acta Autom. Sinica, vol. 43, no. 2, pp. 161–175, Feb. 2017.
[5]	H. Wang, Y. J. Huang, A. Khajepour, Y. Rasekhipour, Y. B. Zhang, and D. P. Cao, " Crash mitigation in motion planning for autonomous vehicles,” IEEE Trans. Intell. Transp. Syst., pp. 1–11, Jan. 2019.
[6]	L. Chen, X. M. Hu, W. Tian, H. Wang, D. P. Cao, and F.-Y. Wang, " Parallel planning: a new motion planning framework for autonomous driving,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 236–246, Jan. 2019. doi: 10.1109/JAS.2018.7511186
[7]	K. T. Leung, J. F. Whidborne, D. Purdy, and A. Dunoyer, " A review of ground vehicle dynamic state estimations utilising GPS/INS,” Veh. Syst. Dyn., vol. 49, no. 1–2, pp. 29–58, Jan. 2011. doi: 10.1080/00423110903406649
[8]	A. Katriniok and D. Abel, " Adaptive EKF-based vehicle state estimation with online assessment of local observability,” IEEE Trans. Control Syst. Technol., vol. 24, no. 4, pp. 1368–1381, Jul. 2016. doi: 10.1109/TCST.2015.2488597
[9]	G. Baffet, A. Charara, D. Lechner, and D. Thomas, " Experimental evaluation of observers for tire-road forces, sideslip angle and wheel cornering stiffness,” Veh. Syst. Dyn., vol. 46, no. 6, pp. 501–520, Jun. 2008. doi: 10.1080/00423110701493963
[10]	R. Anderson and D. M. Bevly, " Using GPS with a modelbased estimator to estimate critical vehicle states,” Veh. Syst. Dyn., vol. 48, no. 12, pp. 1413–1438, Dec. 2010. doi: 10.1080/00423110903461347
[11]	E. Hashemia, S. Khosravani, A. Khajepour, A. Kasaiezadeh, S. K. Chen, and B. Litkouhi, " Longitudinal vehicle state estimation using nonlinear and parametervarying observers,” Mechatronics, vol. 43, pp. 28–39, May 2017. doi: 10.1016/j.mechatronics.2017.02.004
[12]	H. Y. Guo, D. P. Cao, H. Chen, C. Lv, H. J. Wang, and S. Q. Yang, " Vehicle dynamic state estimation: state of the art schemes and perspectives,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 418–431, Feb. 2018. doi: 10.1109/JAS.2017.7510811
[13]	L. Y. Hsu and T. L. Chen, " Vehicle full-state estimation and prediction system using state observers,” IEEE Trans. Vehicular Technology, vol. 58, no. 6, pp. 2651–2662, Oct. 2008.
[14]	J. B. Li, X. L. Liang, and W. Q. Yue, " The vehicle dynamic parameters recognition of in-wheel motor driven electric vehicle,” Veh. Syst. Dyn., vol. 15, pp. 443–447, Dec. 2011.
[15]	H. Mo and T. Wang, " Computing with words in generalized interval type-2 fuzzy sets,” Acta Autom. Sinica, vol. 38, no. 5, pp. 707–715, May 2012. doi: 10.3724/SP.J.1004.2012.00707
[16]	H. Mo and F. Y. Wang, " Linguistic Dynamic Systems and Type-2 Fuzzy logic,” Beijing: China Science and Technology Press, 2013.
[17]	H. Mo, F.-Y. Wang, M. Zhou, R. M. Li, and Z. Q. Xiao, " Footprint of Uncertainty for type-2 fuzzy sets,” Information Sciences, vol. 272, pp. 96–110, Jul. 2014. doi: 10.1016/j.ins.2014.02.092
[18]	H. Mo, K. F. Yan, X. M. Zhao, Y. Q. Zeng, X. Wang, and F.-Y. Wang, " Type-2 fuzzy comprehension evaluation for tourist attractive competency,” IEEE Trans. Computational Social Systems, vol. 6, no. 1, pp. 96–102, Feb. 2019. doi: 10.1109/TCSS.2019.2891306
[19]	L. A. Zadeh, " Fuzzy logic = computing with words,” IEEE Trans. Fuzzy Systems, vol. 4, no. 2, pp. 103–111, May 1996.
[20]	R. M. Li, C. Y. Jiang, F. H. Zhu, and X. L. Chen, " Traffic flow data forecasting based on interval type-2 fuzzy sets theory,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 2, pp. 141–148, Apr. 2016. doi: 10.1109/JAS.2016.7451101
[21]	H. Mo, J. Wang, X. Li, and Z. L. Wu, " Linguistic dynamic modeling and analysis of psychological health state using interval type-2 fuzzy sets,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 4, pp. 366–373, Oct. 2015. doi: 10.1109/JAS.2015.7296531
[22]	X. G. Lu, M. Liu, and J. X. Liu, " Design and optimization of interval type-2 fuzzy logic controller for delta parallel robot trajectory control,” Int. J. Fuzzy Systems, vol. 19, no. 1, pp. 190–206, Feb. 2017. doi: 10.1007/s40815-015-0131-3
[23]	J. M. Garibaldi, " The need for fuzzy AI,” IEEE/CAA J. Autom Sinica, vol. 6, no. 3, pp. 610–622, May 2019. doi: 10.1109/JAS.2019.1911465
[24]	H. Mo, " Linguistic dynamic orbits in the time varying universe of discourse,” Acta Autom. Sinica, vol. 38, no. 10, pp. 1585–1594, Oct. 2012. doi: 10.3724/SP.J.1004.2012.01585
[25]	J. M. Mendel, " Uncertainty, fuzzy logic, and signal processing,” Signal Processing, vol. 80, no. 6, pp. 913–933, 2000. doi: 10.1016/S0165-1684(00)00011-6
[26]	J. R. Aguero and A. Vargas, " Calculating functions of interval type-2 fuzzy numbers for fault current analysis,” IEEE Trans. Fuzzy Systems, vol. 15, no. 1, pp. 31–40, Feb. 2007. doi: 10.1109/TFUZZ.2006.889757
[27]	F.-Y. Wang and H. Mo, " Fundamental problems of type-2 fuzzy sets,” Acta Autom. Sinica, vol. 43, no. 7, pp. 1114–1141, 2017.
[28]	M. Zhou, H. R. Dong, F.-Y. Wang, Q. L. Wang, and X. X. Yang, " Modeling and simulation of pedestrian dynamical behavior based on a fuzzy logic approach,” Information Sciences, vol. 360, pp. 112–130, Sep. 2016. doi: 10.1016/j.ins.2016.04.018
[29]	M. Zhou, H. R. Dong, D. Wen, X. M. Yao, and X. B. Sun, " Modeling of crowd evacuation with assailants via a fuzzy logic approach,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 9, pp. 2395–2407, Mar. 2016. doi: 10.1109/TITS.2016.2521783

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(11)

Get Citation

PDF

XML

Article Metrics

Article views (1531) PDF downloads(76)

Type-2 Fuzzy Control for Driving State and Behavioral Decisions of Unmanned Vehicle

doi: 10.1109/JAS.2019.1911810

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content