Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2021 , Vol. 55 >Issue 10: 1310 - 1319

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.240

Multi-Object Fuzzy Control Strategy of Unmanned Driving Robot

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  • School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received date: 2020-07-29

  Online published: 2021-11-01

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Abstract

In order to realize the smooth control of vehicle by unmanned driving robot (UDR) in paths with different curvatures, a control strategy for UDR based on multi-objective fuzzy decision is proposed. First, the integrated dynamics models of the driving robot and vehicle are established. Then, a yaw rate generation method and a multi-objective fuzzy decision coordinated manipulation strategy are established. The yaw rate generation method generates the reference yaw rate according to the speed and path required by the test while the multi-objective fuzzy decision coordinated manipulation strategy generates sets of target speeds and target yaw rates according to the current speed. Finally, decisions are made on the scheme in the set under multiple constrains. The best scheme is chosen as the target speed and target yaw rate of the next moment. The test and simulation results demonstrate the effectiveness of the proposed strategy.

Key words： driving robot; multi-objective decision; manipulation strategy

Cite this article

QI Dongrun, CHEN Gang . Multi-Object Fuzzy Control Strategy of Unmanned Driving Robot[J]. Journal of Shanghai Jiaotong University, 2021 , 55(10) : 1310 -1319 . DOI: 10.16183/j.cnki.jsjtu.2020.240

References

[1] CHEN G, CHEN S B, LANGARI R, et al. Driver-behavior-based adaptive steering robust nonlinear control of unmanned driving robotic vehicle with modeling uncertainties and disturbance observer[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8):8183-8190.
[2] 吴俊, 陈刚. 驾驶机器人车辆的多模式切换控制[J]. 汽车工程, 2018, 40(10):1215-1222.
[2] WU Jun, CHEN Gang. Multi-mode switching control for robot driven vehicles[J]. Automotive Engineering, 2018, 40(10):1215-1222.
[3] 顾爱博, 陈刚. 驾驶机器人车辆多新息动态转向力矩补偿[J]. 西安交通大学学报, 2020, 54(7):43-51.
[3] GU Aibo, CHEN Gang. Multi-innovation based dynamic steering torque compensation for driving robot vehicle[J]. Journal of Xi’an Jiaotong University, 2020, 54(7):43-51.
[4] CHEN G, ZHANG W G. Hierarchical coordinated control method for unmanned robot applied to automotive test[J]. IEEE Transactions on Industrial Electronics, 2016, 63(2):1039-1051.
[5] ZHU Y H, FU Z Y, FU Z, et al. Multi-features fusion for fault diagnosis of pedal robot using time-speed signals[J]. Sensors, 2019, 19(1):163.
[6] 杨琼琼, 孔斌, 朱勇军. 辅助实现机器人驾驶车辆的研究与仿真[J]. 计算机系统应用, 2014, 23(12):154-159.
[6] YANG Qiongqiong, KONG Bin, ZHU Yongjun. Research and simulation of assist robot driving vehicle[J]. Computer Systems & Applications, 2014, 23(12):154-159.
[7] HIRATA N, MIZUTANI N, MATSUI H, et al. Fuel consumption in a driving test cycle by robotic driver considering system dynamics[C]// 2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ, USA: IEEE, 2015: 3374-3379.
[8] WONG N, CHAMBERS C, STOL K, et al. Development of a robotic driver for autonomous vehicle following[J]. International Journal of Intelligent Systems Technologies and Applications, 2010, 8(1/2/3/4):276-287.
[9] WONG N, CHAMBERS C, STOL K, et al. Autonomous vehicle following using a robotic driver[C]// 15th International Conference on Mechatronics and Machine Vision in Practice. Piscataway, NJ, USA: IEEE, 2008: 115-120.
[10] 汪若尘, 魏振东, 叶青, 等. 视觉预瞄式智能车辆纵横向协同控制研究[J]. 汽车工程, 2019, 41(7):763-770.
[10] WANG Ruochen, WEI Zhendong, YE Qing, et al. A research on visual preview longitudinal and lateral cooperative control of intelligent vehicle[J]. Automotive Engineering, 2019, 41(7):763-770.
[11] 蔡英凤, 李健, 孙晓强, 等. 智能汽车路径跟踪混合控制策略研究[J]. 中国机械工程, 2020, 31(3):289-298.
[11] CAI Yingfeng, LI Jian, SUN Xiaoqiang, et al. Research on hybrid control strategy for intelligent vehicle path tracking[J]. China Mechanical Engineering, 2020, 31(3):289-298.
[12] 曹阳, 贺登博, 喻凡, 等. 基于主动转向的车辆路径跟随广义预测控制[J]. 上海交通大学学报, 2016, 50(3):401-406.
[12] CAO Yang, HE Dengbo, YU Fan, et al. Generalized predictive control based on vehicle path following strategy by using active steering system[J]. Journal of Shanghai Jiao Tong University, 2016, 50(3):401-406.
[13] 章军辉, 李庆, 陈大鹏. 仿驾驶员多目标决策自适应巡航鲁棒控制[J]. 控制理论与应用, 2018, 35(6):769-777.
[13] ZHANG Junhui, LI Qing, CHEN Dapeng. Drivers imitated multi-objective adaptive cruise control algorithm[J]. Control Theory & Applications, 2018, 35(6):769-777.
[14] 赵树恩, 冷姚, 邵毅明. 车辆多目标自适应巡航显式模型预测控制[J]. 交通运输工程学报, 2020, 20(3):206-216.
[14] ZHAO Shu’en, LENG Yao, SHAO Yiming. Explicit model predictive control of multi-objective adaptive cruise of vehicle[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3):206-216.
[15] RAKSINCHAROENSAK P, NAGAI M S, SHINO M. Lane keeping control strategy with direct yaw moment control input by considering dynamics of electric vehicle[J]. Vehicle System Dynamics, 2006, 44(Sup.1):192-201.
[16] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 中华人民共和国国家标准: 机动车运行安全技术条件. GB 7258—2017[S]. 北京: 中国质量标准出版传媒有限公司, 2017.
[16] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. Standardization Administration of the People’s Republic of China. National Standard of the People’s Republic of China: Technical specifications for safety of power-driven vehicles operating on roads. GB 7258—2017[S]. Beijing: China Quality and Standards Publishing & Media Co., Ltd., 2017.
[17] 高建平, 郭忠印. 基于运行车速的公路线形设计质量评价[J]. 同济大学学报(自然科学版), 2004, 32(7):906-911.
[17] GAO Jianping, GUO Zhongyin. Evaluation of highway alignment design quality based on operating speed[J]. Journal of Tongji University, 2004, 32(7):906-911.
[18] 尹念东. 汽车—驾驶员—环境闭环系统操纵稳定性虚拟试验技术的研究[D]. 北京: 中国农业大学, 2001: 70-74.
[18] YIN Niandong. Study of virtual experiment on handling behavior of driver-vehicle-environment closed-loop system[D]. Beijing: China Agricultural University, 2001: 70-74.
[19] International Organization for Standardization (ISO). Passenger cars—Test track for a serve lane-change manoeuvre—Part 1: Double lane-change: ISO 3888-1: 2018(E)[S]. Switzerland: ISO, 2018: 1-3.
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